Game-based sensorimotor rehabilitator

ABSTRACT

Treatment of neurological injury through motor relearning. A game-based sensorimotor rehabilitator that enables individuals to interact with the functional objects using the appropriate amount of force, tilt, finger movement, and muscle activity to regain lost skill due to injury.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-In-Part of US ApplicationPCT/US2014/038124, filed May 15, 2014, incorporated herein by referencein its entirety, which claims priority from Provisional Application U.S.Application 61/824,258, filed May 16, 2013, incorporated herein byreference in its entirety. This application claims priority fromProvisional Application U.S. Application 62/152,315, filed Apr. 24,2015, incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

Even basic tasks such as feeding ourselves, manipulating tools, andperforming activities of daily living require some skill. Usingspecialized tools, for example by machinists such as welders, musicians,chefs and surgeons requires even more skill acquired through motorlearning which requires extended practice. One main event that canimpact the ability to perform these tasks is stroke.

There are over 8 million stroke survivors in the US. Majority of them donot have access to rehabilitation and have persistent hand dysfunctionleading to chronic disability. Recovery of hand function afterneurological injury such as stroke, cerebral palsy, multiple sclerosis,spinal cord injury etc. is extremely challenging. Recovery occursthrough motor re-learning during which specific sensory-motorassociations are formed to shape hand posture to match that of theobject, and scale fingertip forces to the weight and texture of objects.These associations need to be fine-tuned through practice andestablished in long-term procedural memory to regain skill. Howeverforming such task-specific memory requires flexible interaction withvarious types of objects in a systematic manner, appropriately rewardedfor accuracy that can be repeated without becoming tiresome.Furthermore, it is challenging to facilitate the formation of specificsensory-motor associations because individuals tend to use compensatorystrategies, such as increasing the abduction angle at the shoulder, andexcessively co-activating the flexor and extensor muscles across a jointwhen attempting to complete the task. These compensatory strategiesreinforce abnormal movements which makes it more difficult to regainskill in the long term.

Hemiparesis is the most common impairment after stroke and typicallyaffects the upper extremity more than the lower extremity. Studiesindicate that upper-extremity weakness, spasticity, and abnormal motorsynergies are insufficient to explain the impairment in reachingmovements after stroke (Twitchell, 1959; Wing et al., 1990; Roby-Bramiet al., 1997), and suggest that additional higher-order control deficitsmay be present (Beer et al., 1999).

A well-characterized paradigm for the study of higher order sensorimotorintegration in hand motor control is to measure subject's ability toanticipate the fingertip forces required to grasp and lift objects(Johansson, 1996). Anticipatory (feed-forward) fingertip force controlensures the generation of appropriate grip and load forces so as toavoid crushing delicate objects or dropping heavy ones, and is thoughtto be based on the formation of internal models of object properties inthe central nervous system (Johansson and Westling, 1988; Gordon et al.,1993; Flanagan, 1999; Davidson and Wolpert, 2004). Anticipatory controlof grasp is reflected in the ability to scale peak grip force rates(GFR) and peak load force rates (LFR) to the texture and weight ofobjects before confirmatory feedback becomes available (Johansson andWestling, 1988; Flanagan et al., 2001). Healthy subjects are able toappropriately scale peak force rates to object properties after just oneor two lifts, and accurately recall those 24 hours later (Gordon et al.,1993; Flanagan et al., 2001).

Planning of precision grasp was assessed by measurement of anticipatoryscaling of peak LFR and peak GFR to object weight, as the peak amplitudeof these variables is scaled to the expected weight of the object beforesensory feedback signaling the object's weight is available at lift-off(Johansson and Westling, 1988; Gordon et al., 1993; Flanagan et al.,2001). Scaling of the peak force rate ensures that the time to producelifting forces does not increase linearly with object weight. Precisiongrasp execution was assessed by measurement of the timing and efficiencyof grip-load force coordination, as these variables indicate the degreeof fine motor control necessary for precision grasp (Forssberg et al.,1999). Transfer paradigms are likely to give us a better understandingof how information is exchanged between the two hemispheres and may haveimportant implications for the development of rehabilitation strategiesthat incorporate practice with the non-involved hand prior to practicewith the involved hand to improve grasping behavior after stroke(Raghavan et al, 2006).

The ability to predict and anticipate the mechanical demands of theenvironment promotes smooth and skillful motor actions. Thus, the fingerforces produced to grasp and lift an object are scaled to the physicalproperties such as weight. Information about the relevant objectproperties can also be inferred from visual cues. A particularlyimportant cue is the size of the object, which enables an estimation ofthe weight when the material is known. It has been frequentlydemonstrated that grip and load forces indeed anticipate object size(Gordon et al. 1991a, b; Cole 2008; Li et al. 2009). In addition tosize, other physical object characteristics determine the grip forcenecessary to hold an object. Thus, friction at the finger-object contactis crucial and it has been shown that changes in the objects surfacematerial with altering friction are precisely anticipated on the basisof the last lifting trial (Cadoret and Smith 1996; Flanagan andJohansson 2002; Johansson and Westling 1984).

Motor learning has been shown to occur over multiple time-scales. Atleast three underlying processes are thought to contribute to learning:(1) error-based adaptation (fast process), (2) repetition that altersmovement biases depending on what is repeated (slow process), and (3)reinforcement that occurs when error is reduced successfully and leadsto savings or faster re-learning on subsequent attempts. Currentlyavailable interactive platforms do not facilitate real-time interactionwith kinesthetic and haptic feedback in a controlled and paced mannerfor rehabilitation. There is a need for systems and methods to enhancemotor re-learning for restoration of hand function, especially afterstroke. In particular, there is a need for a low cost commercial devicethat can measure grip and load forces applied by the subjects to measuredexterity. There is also a need for systems and method of statisticalanalysis for interpreting clinical data from such devices for thepurpose of diagnosis of the extent of hand dysfunction, prognosticateimprovement with specific types of therapy and to provide feedback andmetrics on degree of improvement.

SUMMARY OF THE INVENTION

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the following drawings and thedetailed description.

In one implementation, the invention includes systems and methods tohelp restore dexterity and functional hand use in patients withneurologic impairment from various conditions, for example, stroke,cerebral palsy, spinal cord injury, multiple sclerosis etc. Theobjective of this game-based physical therapy schema is to engageindividuals using a biofeedback strategy to facilitate a patient's brainto develop alternate neural pathways to overcome the damage produced bythe stroke. The game will provide an in-home option to encourage thepatient to perform therapy more frequently to hasten their improvementand minimize the stress associated with travel to therapy centers. Theconcept leverages current interest in computer games on smallinexpensive wireless communication devices with sophisticatedbiomechanical algorithms to generate clinical metrics of performance andbiofeedback. Data from the biomechanical analyses will be accessible toone or more providers and be interfaced with the electronic medicalrecord for their input and direction. The schema can also be adapted togenerate training platforms for patients with other types of musculardeficits and in intact individuals.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features of the present disclosure will becomemore fully apparent from the following description and appended claims,taken in conjunction with the accompanying drawings. Understanding thatthese drawings depict only several embodiments in accordance with thedisclosure and are, therefore, not to be considered limiting of itsscope, the disclosure will be described with additional specificity anddetail through use of the accompanying drawings.

FIG. 1 Schematic of the components of the device.

FIG. 2 Parts used to create device components.

FIG. 3 Sensorized game controller shaped like real objects that need tobe manipulated; e.g. a coffee cup, a stick, cooking and eatingimplements, grooming tools, writing tools, adapted typing and musicalkeyboards, objects of various shapes, sizes, weights and textures.

FIG. 4 is a graph illustrating peak load force rate linearly scaled toweight and activity in lifting muscles.

FIGS. 5A-5B illustrates the determination of scaling error for a giventrial across weights (FIG. 5A) and trial-to-trial variability (FIG. 5B)to measure successful adaptation and savings.

FIG. 6A is an image of a virtual human grasping a cylinder, FIG. 6B is aschematic of a finger grasping a cylinder; FIG. 6C is a schematicmapping the joints of a hand.

FIG. 7 illustrates graphs of preliminary results with time profiles ofmeasured forces from sensors (left) and torques at each finger joint(right).

FIG. 8 illustrates a sensorized cuff and a sensorized sleeve.

FIG. 9 illustrates a sensorized vest.

FIG. 10 illustrates an embodiment of a computer system of the presentinvention.

FIG. 11 illustrates anticipatory muscle activation as it reducesabnormal directional biases and increases grasp efficiency.

FIG. 12 illustrates a graph showing error reduction rates—reflectivelearning − with adaptation and repetition, with + indicating presenceand − indicating absence.

FIGS. 13A-C illustrate congruent hand practice impact on adaptationrestoration and muscle control in the affected hand post-stroke.

FIGS. 14A-B illustrates symptomatic pianists showing reduced activity inthe upper and lower trapezius (LT) and increased activity in the fingerextensor (EDC) at baseline compared with asymptomatic pianists. With LTactivation, the slope (m) between EDC and LT in symptomatic pianistsshifts closer to asymptomatic group compared to baseline (b). FIG. 14Aillustrates normalized muscle activity, FIG. 14B illustrates theextensor digitorum communis.

FIG. 15 is a graph of the relationship between lower trapezius activityand grip force at lift during grasping with the affected handpost-stroke.

FIGS. 16A-16B illustrate graphs of alternate hand training improving theadaptation of fingertip forces to object weight ratio FIG. 16A and graspexecution FIG. 16B.

FIG. 17 illustrates an implementation of a system with a computingdevice in communication with a controller and game device.

FIG. 18 illustrates an implementation of a dorsal glove.

FIGS. 19A-19B show linear relationships between variables measuringgrasp adaptation and object weight and texture.

FIGS. 20A-20B show similar difference in peak force rates for specifiedweight and texture pairs in healthy individuals.

FIGS. 21A-21C show correlation between the Slip Ratio and Peak GripForce Rate for a series of lifts of different textured objects performedwith bare hands, a thin film of Tegaderm over the fingertips, and acoating of foam over the fingers.

FIG. 22 is a circuit diagram of one embodiment of a mechatronicrehabilitator.

FIG. 23A illustrates an electrical circuit with a switch; FIG. 23Billustrates a graph for the performance of the circuit of FIG. 23A.

FIG. 24A shows the construction of a Resistance-Capacitor circuitryimplemented to determine the resistance of flexi-force sensor. For thegiven circuit, we measure the charge/discharge time of the capacitor andcorrelate that to the flexi-force resistance, which in turn incorrelated to the amount of force applied on it. This circuit is used tomeasure the force applied (load force and grasping forces). FIG. 24Bshows the relationship between the (1000/RC) vs. mass placed on thesensors. From the sensors property of resistance being inverselyproportional to the force, it follows that the force is directlyproportional to the inverse of the resistance. The above Figure showsthis to be the case.

FIG. 25A illustrates Mass (Force) vs. 1000/RC-Time (1/R). The sameResistance-Capacitance circuit was used to determine the loading andunloading characteristics of the device. The loading and unloading dataobtained for systematic weights were linearly regressed to obtain apolynomial function to be implemented in code. FIG. 25B shows a circuitimplementation of Flexi-force sensors with a linear low-poweroperational amplifier MCP-6004. Upon observation of previous plots inFIG. 24B and FIG. 25A can be observed that the sensors' readings atclose to zero are not accurate. The circuit of FIG. 25B was implementedto address this.

FIG. 26 illustrates results for an experiment with a 250 gram mass and ahealthy subject. FIG. 26 shows the plot obtained from load forces, gripforces and corresponding load force rates for trials obtained fromhealthy subjects lifting objects with mass of 250 and 500 grams. FIG. 26computes load force rates by direct differentiation after implementationof moving average filter.

FIG. 27 illustrates results for an experiment with a 500 gram mass and ahealthy subject. FIG. 27 shows the plot obtained from load forces, gripforces and corresponding load force rates for trials obtained fromhealthy subjects lifting objects with mass of 250 and 500 grams. FIG. 26computes load force rates by direct differentiation after implementationof moving average filter.

FIG. 28 is a graph of the total mass over time showing the unloadingcharacteristics of the device. This device implements the design of atriangular coaster placed on three flexi-force sensors whose values aresummed to obtain the loading and unloading forces.

FIG. 29A shows a prototype of the interactive task platform and FIG. 29Bshows a display for training of adaptation of grasping and liftingforces.

FIG. 30 shows rate of change in load force when lifting objects ofdifferent weights: The load force profiles of a subject grasping thegrip device of two different weights with precision grip and lifting itwith the dominant hand are shown in solid line. The derived force ratecurves are shown in dashed line. Black indicates heavier object and greyindicates lighter objects. The peak Load Force Rate is defined as thehighest point in the force rate profile.

FIG. 31A and FIG. 31B Dist(A) is the true density of 0 under the nullhypothesis. In grey are 100 realizations of Dist(B) under the null basedon the proposed method using a single copy of simulated training set asthe basis for the realized density estimate.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and made part of this disclosure.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for thesake of clarity.

The description of illustrative embodiments is presented for purposes ofillustration and of description. It is not intended to be exhaustive orlimiting with respect to the precise form disclosed, and modificationsand variations are possible in light of the above teachings or may beacquired from practice of the disclosed embodiments. It is intended thatthe scope of the invention be defined by the claims appended hereto andtheir equivalents.

The purpose of the present invention is to create a portable therapeuticplatform to facilitate skill training/retraining anywhere, anytime usinga novel game controller, such as a game-based sensorimotorrehabilitator, that serves as a virtual coach to provide skill trainingin both healthy and in neurologically impaired individuals usingreal-time tactile, kinesthetic and visual feedback. One implementationis comprised of three components: 1) the game controller, such as a cupor ball or other tool or implement, 2) a microcontroller, and 3) acomputing device, such as a handheld/laptop/desktop computer runninggame-analysis software. In one implementation, illustrated in FIG. 1,sensor information 101 is provided from the game controller to amicrocontroller 102, which may be on the game controller or separate.The microcontroller 102 determines the game controller's state, e.g.force being applied, orientation, acceleration, velocity, etc. Themicrocontroller 102 determines if the game controller's state is withina predetermined set of ranges for a particular application, for exampletilt of the game controller in a simulation involving a cup. Themicrocontroller 102 provides information to a computing device 103 andto a feedback device 104.

In one aspect, an interactive gaming platform is implemented on ahandheld, laptop or desktop computer systems with web access. The systemmay include software-based portions and a hardware-in-the-loop interfacethrough a real object. It is anticipated that such will have wideapplicability in homes, gyms, and rehabilitation centers for patientswith neurologic impairment from various conditions, e.g., stroke,cerebral palsy, spinal cord injury, multiple sclerosis, etc. Asuccessful rehabilitation outcome will restore dexterity and flexiblefunctional hand use with wide access to facilitate tele-diagnostics andtele-treatment through easy modification of gaming parameters. Certainimplementations provide systems and methods to restore adaptation,facilitate grasp efficiency and normal directional biases duringrepetition and enhance the rate of learning to improve hand function andquality of life post stroke.

Motor adaptation occurs when sensory information relevant to the task isextracted to form sensorimotor associations, which are used to predictaccurate responses to similar actions in the future. Adaptation is thepivotal process in that it utilizes error feedback to identify theoptimal movement for a task faster, which when reinforced throughrepetition can enhance learning for sustained changes in skill. It hasbeen noted that patients are unable to adapt their fingertip forces andmovements predictively to the expected consequences of an action withthe affected hand post-stroke, perhaps because sensorimotor informationfrom the affected side is inaccurate and/or its integration isdisrupted.

However, somatosensory and visual information from each side of the bodyis processed bilaterally, and interlimb coordination is mediated bymotor representations in the parietal and premotor areas shared by bothlimbs. Early in recovery after stroke, the undamaged hemisphere showsincreased activation, but eventually normal sensorimotor lateralizationis restored in the stroke-affected hemisphere. This suggests thatredundant homologous pathways in the intact hemisphere facilitatereorganization within the affected hemisphere by mechanisms such asunmasking projections from the intact motor cortex to the cervicalspinal cord, and axonal sprouting and formation of novel subcorticalprojections. In the chronic post-stroke period, independent fingermovements of the recovered hand show a bilateral increase in regionalcerebral blood flow in the dorsolateral and medial premotor areas, whichare involved in motor planning. Disruption of activity in the dorsalpremotor cortex of the intact hemisphere results in degraded behavior inthe paretic hand. Thus, sensory information from each hand isrepresented bilaterally, providing redundant circuits that can beharnessed for recovery. These results suggest that actions from eachhand are represented bilaterally and representations in the intacthemisphere can facilitate planning and control in the affected hand poststroke. Therefore, certain implementations utilize practice with theunaffected hand followed by practice with the affected hand to achieveadaptation of fingertip forces.

First, alternate hand training is provided to restore adaptation offingertip forces in the affected hand. Skilled hand function requiresthat users grasp objects of various weights, textures, and shapes. It isbelieved that successful transfer of adaptation from the unaffected handto the affected hand occurs readily when task-relevant sensoryinformation is provided. This information may be kinesthetic, tactile orvisual, and can be provided using alternative hand practice to formaccurate sensorimotor associations. The understanding of how sensorymodalities interact, given existing sensorimotor impairments, informsthe structure of effective training protocols for adaptation.

Second, postural strategies are delineated to enhance alternate handtraining for increased movement efficiency. Adaptation occurs bydetecting error and then correcting for it predictively on subsequentpractice. Short-latency spinal reflexes and intrinsic biomechanicalproperties of muscles contribute to reactive error correction mechanismswhich adjust motor output for novel or unplanned movements. Thesereactive mechanisms are accentuated post stroke due to centraldisinhibition, leading to spasticity, synergistic movement patterns,inappropriate muscle co-activation, and use of excessive grip forces,all of which reduce the quality of movement-related sensory feedback andaffect learning. However, error correction mechanisms also includesupraspinal long-latency reflexes that take into account theconsequences of the net torques on interconnected joints, and reflect aninternal representation of the dynamics of the entire limb. Facilitationof these long-latency mechanisms can generate anticipatory posturalresponses that may reduce the need for reactive error correctionmechanisms (FIG. 11). The affected hand 1101 of the individual in FIG.11 exhibits improved movement efficiency based upon the methods appliedbased on the long-latency in the lower trapezius observed in theunaffected hand's 1111 movement.

Stimulation of forearm and hand afferents has been shown to evokelong-latency reflexes in the lower trapezius muscle—a scapular adductorand anti-gravity upper-limb stabilizer. Voluntary activation of thetrapezius further increases the amplitude of the long-latency reflex.Tasks that demand greater dexterity produce even larger bilateralresponses in the trapezius and other upper limb stabilizers in healthyindividuals, but this modulation is disrupted after stroke. Studies onpost-stroke reaching have revealed abnormal joint coupling and muscleco-activation patterns that produce inefficient movements. However,these abnormal patterns are modifiable by the use of gravity loading andtrunk restraint, suggesting that postural stability can affect theprocessing of peripheral movement-related proprioceptive information.For example, studies on skilled pianists show that voluntary activationof the lower trapezius muscle, assisted by biofeedback, leads to greaterefficiency of finger movements during playing. In addition, recentpreliminary data show that lower levels of postural muscle activationare associated with higher grip forces after stroke.

Spasticity and abnormal motor synergies make repetition challengingafter stroke. It is believed that activation of anti-gravity upper limbstabilizing postural muscles (e.g. lower trapezius) is associated withincreased movement efficiency. Thus, enhancing alternate hand practicewith postural muscle activation may reduce abnormal post-strokedirectional biases and facilitate repetition of more efficientmovements. It is believed that triggering active postural strategiesthrough lower trapezius stimulation will increase grasp efficiency. Asfurther described below, certain implementations measure fingertipforces, arm compensation, and electromyographic (EMG) activity in armand back muscles to assess directional biases.

Third, certain implementations determine and utilize learning ratesacross multiple time-scales and stages of skill learning for improvementin hand function post stroke. Adaptation of sensory-motor mappings is afast learning process, which leads to a rapid reduction in movementerror, and typically takes only a few trials; however it is easilyforgotten. Repetition, on the other hand, is an error-independentprocess, and leads to a slow tuning of directional biases toward therepeated movement. For example, the rate of within-session andbetween-session learning across stages of skill learning with ‘enhancedalternate hand training’ can be examined and compared to training withthe affected hand alone, using a novel task panel and structuredtraining protocols. Successful motor learning requires a combination offast and slow processes (FIG. 11). Appropriate sensory-motor mappingslearned through adaptation must be reinforced over time throughrepetition across various stages of motor learning. Early learning(Stage 1) is thought to depend on attentional mechanisms. Attention tospecific task features or sources of sensory information (e.g.kinesthetic, tactile and visual) at this stage may assist with learning.The next associative stage (Stage 2) is characterized by formation ofvisuomotor and sensorimotor associations through trial-and-errorpractice. At this stage, transfer of learning from one limb to the otheroccurs readily. Associations formed by using the unaffected hand can beused to improve the associations formed with the affected hand. In laterlearning (Stage 3) the movement is fine-tuned and becomes automatic.This stage of learning would require repeated practice with the affectedhand which will lead to greater movement efficiency. Training algorithmsare thus developed for each stage of learning.

Certain implementations provide a solution for re-learning of skilledhand function where the individual can learn at their own pace withguidance from the game-based sensorimotor rehabilitator (SMR). Thesystems and methods enable the individuals to learn to interact withfunctional objects using just the right amount of force, tilt, fingertorques, and muscle activity to regain lost skill.

In one implementation, real objects are customized with force,orientation and acceleration sensors and the object is virtualized onscreen. As the individual manipulates the object or objects, visualfeedback is provided regarding the appropriateness of fingertip forces,and orientation in a systematic manner to facilitate learning of thecorrect associations. The force and orientation information is also fedto computational biomechanical models, and software algorithms to informthe thresholds for visual feedback. Further, in certain implementations,wearable position and muscle sensors embedded in a dorsal glove, cuff,sleeve or vest at key areas also send information regarding limbposition to the computational models and algorithms. The output of thealgorithms is be sent to actuators located on the glove/cuff/sleeve/vestto provide tactile feedback to key areas in the same manner that ateacher or coach would apply a gently touch to provide a cue to move ina certain manner. Tactile feedback will turn on or off when theindividual moves within or outside the set parameters of the movement.Real time, trial-to-trial information from interaction with the objectcan be stored and communicated to the provider's electronic medicalrecord for diagnosis, documentation of progress, and fine-tuning ofstimulus parameters and gaming and feedback levels provided through aservice provider. Various implementations utilize methods for how andwhen practice should be facilitated with one hand, or both handsseparately, alternately, or simultaneously.

One implementation of a device for facilitating rehabilitation andtraining described above incorporates several components as shown in theflow chart of FIG. 1, using the parts shown in FIG. 2. FIG. 2 shows oneimplementation of a rehabilitation system 200. A microcontroller 201 isconfigured to receive sensor information from force sensors 211, whichmay be part of a game controller physically manipulated by the user. Aninertial measurement unite (IMU) is in communication with themicrocontroller to provide inertial information, such as gyroscopic,accelerometer, and magnetometer information. The microcontroller 201 isfurther in communication with a feedback mechanism 213. Themicrocontroller 201 may also utilize a wireless communication unit 203,such as to communicate with a second microprocessor 205. Themicroprocessor 201 is ultimately in communication with a computingdevice 207, such as a P.C. or laptop.

In one implementation, the game controller may have a form factorsimilar to a functional object, such as a coffee cup or ball. In furtherimplementation the controller may consists of an object to bemanipulated, such as a cup, and a wearable device for the user such as avest. The controllers are objects with various shapes and sizes equippedwith force sensors, position/orientation sensors, inertial measurementunits, and vibrational modules (FIG. 3). FIG. 3 shows a cup-like gamecontroller 301 having a cylindrical body. Force sensors 303, such asforce sensitive resistors, are positioned to correspond with a user'shand. For example, the force sensors may be positioned at multiplelocations to correlate with fingertip and palm placement on the gamecontroller 301. A separate feedback mechanism 320 is provided, such as astrap or bracelet, to provide feedback regarding the position of thegame controller 301.

FIG. 8 illustrates another implementation where the system 800 includesa sleeve 810 as part of the game controller. The sleeve 810 includesposition sensors 805 as well as a wireless transmitter 801. The sensors805 may be positioned to indicate the position of the upper arm andlower arm. Vibro-tactile actuators 803 are included on the sleeve 810,for example on the flexor and extensor portions of the arm. A cuff 830may also be provided for further vibro-tactile feedback viavibro-tactile actuators 803.

FIG. 9 illustrates another implementation where the system 900 includesa vest 910 as part of the game controller. The vest 910 includesposition sensors 905 as well as a wireless transmitter 901. The sensors905 may be positioned to indicate the position of the trunk.Vibro-tactile actuators 903 are included on the vest 910, for examplecorresponding to the lower trapezius and pectoralis muscle positions.

The force sensors are, in one implementation, oriented orthogonally tomeasure both grip and load forces, and the rate of change of theseforces will be computed. Certain patient specific game controllersinclude software to communicate, such as wirelessly, to a computer. Theinformation from the sensorized game controller is also integrated withpositional and muscle recruitment information from sensors sewn into acuff/sleeve/vest worn by an individual in certain implementations. Thecontrollers include one or more of sensors and feedback systems. Thecontrollers may be adapted for specific treatment regimes. For example,a controller may provide information to software on an associatedcomputer regarding the grip force, movement speed, and movementdirection of an object the user is interacting with. The software maythen provide feedback to the user through the controller, such asvibrotactile actuators.

One implementation of the game controller comprises a cylinder similarin size to a coffee cup and instrumented with two pressure transducerspositioned at the thumb and middle finger locations and 3D spatialpositioning transmitter. Foam or other tactile-deadening material, maybe used on the game controller to prevent tactile information for theuser, such as to simulate an injury. The micro-computer interfaceincludes circuitry to acquire force data from force transducers and 3Dposition transducers. The controller communicates wirelessly with thegame based software on the laptop that interacts and controls gameavatars representing a real-world object. The microcomputer interfaceand sensors are incorporated within in the controller. The softwaredevelopment uses a 3D game engine's flexible input capacity to drive aninteractive visualization of the patient's grip force with game-likereward structures and feedback systems.

As seen in FIG. 17 above, the current prototype's game objective is toask the individual, represented by an avatar, to squeeze the foamcylinder until the Avatar can open his can of spinach. It will beappreciated that pop-culture or other such references can be used toprovide simple clues for the associated functionality as well asfamiliarity for users. FIG. 17 shows a simplified device layout of FIG.2, where a system 1701 includes a game controller 1710 in communicationwith a controller 1701 serving as an interface between the gamecontroller 1710 and a computing device 1707 (shown as a laptop). Themicrocontroller's imbedded data acquisition code provides forcommunication, such as continuously monitors the pressure transduceroutputs and streams data, to the laptop's game-analysis software that,in turn, controls the avatar and provides an interactive, engaging andfun experience for the patient. In one embodiment, the communication isby USB. The laptop software communicates to the patient using visual andaudio cues provided by the game concept. The microcontroller acquiresanalog voltages associated with pressure transducer response and thentransmits raw temporal force data, i.e. force changes over time for eachtransducer during the training session. Circuitry within themicrocontroller a) acquires the analog voltage signals from the pressuretransducers; b) performs signal filtering and analog to digitalconversion; c) for embodiments using a serial communicationtransmission, such as RS-232c protocol, the circuitry adds a time stampto each data pair using the micro-controller's system clock and thentransmits the data stream via RS-232C serial format to the laptop. Thisserial data stream is captured by sub-routines within the laptop's gameprogram that interprets the force and translates it into “Popeyesqueezing the can”.

In another implementation, interaction with the interactive “cupcontroller” 1710 provides vibro-tactile feedback in addition to visualand auditory feedback. This version consists of an instrumented “cupcontroller” 1710 and a wrist band (not shown in FIG. 17) withvibro-tactile actuators. The cup controller 1710 is virtualized on ascreen of the computing device 1707 as a cup which needs to be moved inorder to catch balls on the screen. As the patient picks up the cupcontroller 1710, the goal is to learn to use the right amount of fingerforce to grasp the cylinder and the right tilt. If they squeeze too hardthe balls go flying out, if they tilt the cup too far the wristbandprovides directional vibro-tactile feedback: vibration to the right sideof the wrist if the tilt is excessive on the right, and vibration to theleft if the tilt is excessive on the other side.

To facilitate training in a systematic and controlled manner, oneimplementation relates to interactive training algorithms. The trainingalgorithms are based on the principles of ‘learning to learn’, wherelearning simple sensorimotor associations within a low dimensional taskstructure will lead to faster acquisition of similar associations forother tasks. The stepwise introduction of variability and complexityacross the stages of learning will then lead to generalization oflearning to novel tasks. The training will be delivered usinginstructions on the computerized display to the user based on input fromthe sensorized objects and the dorsal glove/cuff/sleeve/vestimplementations. The instructions may cue practice with one or the otherhand, and provide feedback and reward for correct performance. In oneimplementation, a sensorized mat is utilized and the training algorithmis adapted for such sensorized mat to facilitate standardized objectplacement in the work space and a structured progression of trainingfrom simple sensory-motor mappings, to practice with more complexreal-world objects leading to increased overall skill. The trainingalgorithms may be useful for training hand skill in other patientpopulations as well.

In one implementation, a video game platform is provided. A gamecontroller is virtualized on screen, and interfaced with game scenariosto provide visual feedback of the interaction with the object andaugment the feedback based on the output of the analytic algorithms. Thegame scenarios will provide interactive and enjoyable training. Game canbe viewed on a variety of platforms, e.g. iPod, iPhone, iPad, Laptops,and the controller itself.

One implementation utilizes analytic software. During typical operation,the game controller software will continuously monitor force transducerpressures and acceleration of the object from trial-to-trial from eachhand separately for diagnostic information. For example, the user willbe directed to modify the object by changing its weight (w), or changingthe texture (t) of the object-grasp interface. The user will also beasked to hold the object in various ways to gather information on thefinger joint positions to compute estimated torques at themetacarpophalangeal (MCP) and interphalangeal (PIP) joints. FIGS. 6A-Cillustrate a hand grasping a cylinder. The information obtained fromthese directed grasps will be used to set thresholds for visual andtactile feedback.

In one implementation, electronic inputs from position and musclesensors are embedded in wearable modular garments comprising dorsalgloves/cuffs/sleeves/and a vest at key locations to signal the position,orientation and activity at critical positions or muscle activity levelsto provide information about the limb strategy used to manipulate theobject. As a patient grasps the object, electronic inputs from thedevice will be integrated into computational hand/arm models andsoftware algorithms that will (1) integrate information from all theactivated sensors on the object and the wearable garment, (2) processthe information as detailed below to compute performance parameters, (3)send outputs to actuators located on the glove/cuff/sleeve/vest toactivate key areas to provide tactile feedback about how the movement isbeing performed, and (4) transmit the clinical performance data andanalytics to physicians or providers to integrate with the user'selectronic medical record.

In one implementation, a dorsal glove 1800, shown in FIG. 18, isutilized. The dorsal glove 1800 rests on the top of the hand. It isfixed to the palmar surface using adjustable bands 1810. In oneimplementation, the adjustable bands are fixed over the creases of theproximal interphalangeal joints and distal interphalangeal joints.Sensors on the dorsal glove detect finger joint positions and angles ofthe proximal interphalangeal joints, distal interphalangeal joints, andmetacarpophalangeal joints. Vibrotactile actuators embedded on the gloveover the knuckles can trigger changes in position to specificallydetected positions.

The information provided allows for computation of performanceparameters. For example, orthogonally placed force transducers willmeasure the rate of change of load force (vertical force) and grip force(normal force) which will be used to compute scaling error, or themismatch between the forces needed to grasp the object and that actuallyproduced. Ideal performance metrics will be obtained from storednormative data of healthy individuals, or “normal” performance from theunaffected limb.

The error definition for any trial is motivated by the ideal case ofscaling where, given specific weights, a linear relationship is observedin the peak rate of change of the vertical load force (pLFR, FIG. 4).For a given trial, (e.g. Trial 3 in FIG. 5), the N weights in ascendingorder are w₁, w₂, . . . w_(N) with corresponding peak load force ratesF₁, F₂, . . . F_(N). Taking a weight w_(n) and its associated pLFR,F_(n) as the reference pair, w_(n) one can determine the ideal peak loadforce rates for the other weights w_(k) as

${F_{k}^{\prime} = {\frac{w_{k}}{w_{n}}F_{n}}},$

and the mean sum-squared error as

${e_{n} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}\; \left( {F_{k} - F_{k}^{\prime}} \right)^{2}}}},$

where the subscript in e_(n) indicates that the nth weight-pLFR pair isused as the reference unit for the calculation. The scaling error of agiven trial j can then be calculated by averaging e_(n) based ondifferent reference pairs,

${E_{j} = {\frac{1}{{NF}_{RMS}^{2}}{\sum\limits_{n = 1}^{N}\; e_{n}}}},$

where dividing by the normalizing factor

$F_{RMS}^{2} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}\; F_{n}^{2}}}$

will allow comparison of the scaling error between sessions whendifferent weights are applied. Within a session, the reduction rate ofthe scaling error E_(j), across J trials will be a measure for“successful” adaptation. The scaling error will first be computed duringinitial diagnostic trials prior to training. The aim of the trainingsession will be to reduce the mismatch in scaling error between thenormative data and the real time data, and/or between the “unaffectedhand” and the “affected hand” which will be the reference hand data. Thepeak LFR or peak GFR at each training trial will be compared with theaverage error for the given weight of the object from the “normal”trials. A mismatch of >50% will signal augmentation of visual feedbackon the level of grip force and prompt the individual to practice withthe unaffected hand before practice with the affected hand until themismatch is reduced to <10%. If there is no unaffected hand as thereference hand, mismatch will lead to trigger of vibrotactile actuatorsin multiple locations on the flexor and extensor aspect across thewrist, the elbow and back (see below). Testing has shown that themismatch is due to excessive coactivation which can be reduced bylocation specific vibrotactile stimulation.

For certain implementations, trial-to-trial variability in the magnitudeof the pLFR (FIG. 5B) will also be computed independent of the scalingerror as the variance of pLFR divided by the weight (to remove theweight effect). Decrease in trial-to-trial variability will precedesuccessful adaptation. Scaling error and trial-to-trial variability willbe computed on the peak grip force rate (pGFR) for adaptation to textureover every 5 trials. Higher trial to trial variability is correlatedwith increased grip forces to increase the safety margin, whichindicates difficulty with processing of sensory information from theobject. Increased trial-to-trial variability by 50% compared with thatfrom the reference hand will trigger stimulation of vibrotactileactuators in the back of the vest to activate the lower trapezius muscle(FIG. 6) to increase stochastic noise in the system to enhance sensoryprocessing.

In one implementation, figure torques are calculated. The grip force(normal contact force) exerted by each finger, measured by the forcesensors, will be used to map to the joint space in a computationalhand/arm model, which will output the joint torque at each finger joint(each finger has 3 joints: the distal PIP, proximal PIP, and MCP). Thekinetic redundancy of the two fingers due to the muscle-inducedactuations in the closed loop will be resolved using an optimizationalgorithm. Normally the finger torquesare greater at the proximal PIPjoint compared with that in the MCP joint (FIG. 7). Greater fingertorques in the MCP joints of the fingers compared to the proximal PIPjoints signal an inefficient grasp and will trigger vibrotactileactuators in a cuff in the front of the wrist joint to facilitaterelaxation of the muscle and change in finger position. Success will bemeasured by reduction in error between the MCP and PIP joint torquesproduced by the training hand compared with the same from the referencehand.

In one implementation, in a compensatory limb strategy is utilized.Position sensors on the sleeve at the elbow and side of the vest willindicate whether the elbow is close to the trunk or away from it. If theelbow is away from the trunk and the muscle sensor over the lateraldeltoid is activated, it indicates compensation by increasing the angleof the shoulder to orient the hand to grasp the object. This degree ofcompensation will correlate with excessive tilting of the object. Inorder to correct the tilt, the user will have to learn to reduce theshoulder abduction angle by bringing the elbow position sensor andposition sensor on the side of the vest closer together. When thedistance between the elbow and the vest is greater than that in thereference database, vibro-tactile actuators in both locations willvibrate simultaneously and when the distance is reduced, these will stopvibrating indicating that the correct position has been learned. Inanother embodiment the onset of vibration, rather than the stopping ofit, will indicate that the correct position has been achieved.

It should be appreciated that learning across various sessions andstages of learning will be determined by the (1) savings in the timecourse of error reduction for adaptation to object weight and texture,and (2) change in the location of the finger torque from the MCP jointto the PIP joints which is necessary for precision grasp, and (3)reduction in compensation of the limb position.

In one implementation, the data will be stored and/or displayed in realtime to an electronic medical record in the form of trial-to-trial data(graphs), or average data from a training session (in the form of plotsand charts). Alternatively, the data may be stored locally with theuser, such as in memory associated with a user device.

While the individual interfaces directly with the objects through anovel interactive gaming environment, clinicians can monitor performancemeasures, which will be used for both immediate online feedback with orwithout the presence of an expert available remotely, and/or savedoffline as part of the patient training records. This will enableanalysis of the training session online and offline.

In one implementation, a wearable feedback device is provided. Thewearable feedback device may be a vibro-tactile feedback device,embedded in one or more of a dorsal glove, cuff, sleeve, or vest.Snug-fitting band, sleeve and vest or a combination of these in the formof a jacket available in various sizes will have pockets for placementof position sensors, vibro-tactile actuators and wireless muscle andmovement sensing transmitters in specific locations indicated in FIG. 8and FIG. 9. The actuators will be located on the flexor and extensoraspect and will turn on or off based on the output of the softwarealgorithms. In one implementation, the actuators will communicatewirelessly with the electronic interface from the sensorized object andthe gaming platform providing an interactive experience and feedback toaid in rehabilitation.

Certain implementations include methods to facilitate hand-objectinteractions and rehabilitation. It is believed that the systematicfacilitation of controlled hand-object interactions assists in theformation of specific sensory-motor associations in neurologicallyimpaired individuals. The configuration of the training algorithms inconjunction with information from the various sensors will prevent theuse of compensatory strategies such as gripping the object too tightlyor tilting it too much by providing tactile stimulation in key areas. Itwill facilitate practice with one or both hands separately, alternately,or simultaneously. Tasks and stimulus features can be programmed andpresented to the individual using virtual reality and updated based ontrial-to-trial performance. These methods can be used in conjunctionwith peripheral or central electric stimulation to reinforce newmovement patterns.

In one application, the device may be utilized with a healthy individualfor training rather than rehabilitation purposes. Ideal placement of ahand, for example, can be modeled and feedback provided when the handvaries from this model. As one example, placement of hands during pianoplaying can be monitored to provide feedback on physical orientation andhand posture that may not be evident merely from the notes being played.

In one embodiment, shown in FIG. 10, a system 100 is provided. FIG. 10shows an exemplary block diagram of an exemplary embodiment of a system100 according to the present disclosure. For example, an exemplaryprocedure in accordance with the present disclosure can be performed bya processing arrangement 110 and/or a computing arrangement 110. Suchprocessing/computing arrangement 110 can be, e.g., entirely or a partof, or include, but not limited to, a computer/processor that caninclude, e.g., one or more microprocessors, and use instructions storedon a computer-accessible medium (e.g., RAM, ROM, hard drive, or otherstorage device).

As shown in FIG. 10, e.g., a computer-accessible medium 120 (e.g., asdescribed herein, a storage device such as a hard disk, floppy disk,memory stick, CD-ROM, RAM, ROM, etc., or a collection thereof) can beprovided (e.g., in communication with the processing arrangement 110).The computer-accessible medium 120 may be a non-transitorycomputer-accessible medium. The computer-accessible medium 120 cancontain executable instructions 130 thereon. In addition oralternatively, a storage arrangement 140 can be provided separately fromthe computer-accessible medium 120, which can provide the instructionsto the processing arrangement 110 so as to configure the processingarrangement to execute certain exemplary procedures, processes andmethods, as described herein, for example.

System 100 may also include a display or output device, an input devicesuch as a key-board, mouse, touch screen or other input device, and maybe connected to additional systems via a logical network. Many of theembodiments described herein may be practiced in a networked environmentusing logical connections to one or more remote computers havingprocessors. Logical connections may include a local area network (LAN)and a wide area network (WAN) that are presented here by way of exampleand not limitation. Such networking environments are commonplace inoffice-wide or enterprise-wide computer networks, intranets and theInternet and may use a wide variety of different communicationprotocols. Those skilled in the art can appreciate that such networkcomputing environments can typically encompass many types of computersystem configurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments of the invention may also be practiced in distributedcomputing environments where tasks are performed by local and remoteprocessing devices that are linked (either by hardwired links, wirelesslinks, or by a combination of hardwired or wireless links) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

Various embodiments are described in the general context of methodsteps, which may be implemented in one embodiment by a program productincluding computer-executable instructions, such as program code,executed by computers in networked environments. Generally, programmodules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Computer-executable instructions, associated datastructures, and program modules represent examples of program code forexecuting steps of the methods disclosed herein. The particular sequenceof such executable instructions or associated data structures representsexamples of corresponding acts for implementing the functions describedin such steps.

Software and web implementations of the present invention could beaccomplished with standard programming techniques with rule based logicand other logic to accomplish the various database searching steps,correlation steps, comparison steps and decision steps. It should alsobe noted that the words “component” and “module,” as used herein and inthe claims, are intended to encompass implementations using one or morelines of software code, and/or hardware implementations, and/orequipment for receiving manual inputs.

1. Experimental Results

The co-ordination of fingertip motion and forces during objectmanipulation developed by Johansson et. al has served as a model systemof sensorimotor integration for more than 30 years, and is a sensitivetest of fine motor control in various patient populations. Experimentswere built from this work to understand the mechanisms of hand motorimpairment after stroke and design physiologically-based rehabilitationapproaches. The following experiments test the ‘alternate hand trainingstrategy’ which involves practice with the unaffected hand and then theaffected hand, to facilitate adaptation, repetition and relearning torestore hand function in stroke patients. The first step is to determinehow individuals adapt their grasp under various sensory constraints,i.e. presence or absence of kinesthetic, visual and tactile sensorymodalities. This would provide a bench mark, which can then be used totest under which conditions the alternate hand training strategy willrestore adaptation.

a. Training Methods

A foam coating for the fingers effectively impairs tactile sensibilityand eliminates 2-point discrimination in healthy individuals. Oneexperimental protocol was fully developed as show below (Table 1 below).

TABLE 1 Experimental protocol for Aim 1 Kinesthetic Vision TactileExperimental Conditions 1 1 1 1 full vision 2 1 0 1 vision occluded 3 01 1 no lift 4 0 0 1 vision occluded + no lift 5 1 1 0 foam fingers 6 1 00 vision occluded + foam fingers 7 0 1 0 no lift + foam fingers 8 0 0 0no lift + vision occluded + foam fingers

Linearity between variables measuring grasp adaptation and object weightand texture and select weight and texture pairs. FIG. 19A-B show alinear relationship between the peak load force rate and object weightfor a wide range of weights (n=10), and between peak grip force rate andthe friction at the grip surface (measured by the co-efficient offriction, COF) for a wide range of textures (n=14). Equivalent weightand texture pairs were selected and their presentation randomized in theexperimental protocol. FIG. 20A-B show that the selected weights andtexture pairs show relatively similar differences in peak force rates.

Minimize the noise in grip force data due to changes in humidity andtemperature. Grip forces are extremely sensitive to small changes inhumidity and temperature. The ridges on the fingertips serve to increasethe coefficient of friction at the grip surface which then requiressmaller grip forces to grasp the same object. This poses a challengewhen subjects are performing repeated lifts over a period of time evenwhen room temperature and humidity are controlled. Therefore a thin film(e.g. Tegaderm) may be applied over the fingertips. Tactile sensibilitycan be measured by examining the correlation between the coefficient offriction and the grip force rate (as shown in FIG. 19A-B). Thecoefficient of friction can be determined for each individual bycomputing the inverse of the Slip Ratio [ratio of grip force to loadforce)]. FIGS. 21A-C show the relationship between the slip ratio andthe peak grip force rates for a wide range of textures. Note that theslip ratio is in a narrower range when using bare hands and whengrasping the objects with a thin film of Tegaderm over the fingertips.The stronger correlation between the slip ratio and the peak grip forcerate suggests that tactile sensibility was less impaired by Tegaderm,than with the foam coating, which clearly impaired tactile sensibilityas can be seen from the significantly weaker correlation.

b. Preliminary Results:

i. The Alternate Hand Practice Strategy can Restore Adaptation ofFingertip Forces after Stroke.

Adaptation with the affected hand is impaired after stroke, but it canbe temporarily restored after prior practice with the unaffected hand.The mechanisms underlying lack of adaptation of fingertip forces toobject weight [measured by the difference in peak load force rate (pLFR)for two weights] were examined by considering muscle activity usingsurface and intramuscular (from FCU, ECRL & BRD muscles to avoidcross-talk) electrodes. 14 patients with post-stroke hemiparesis andage-matched controls lifted an instrumented grip object equipped withforce sensors. On the first lift, the pLFR was not scaled to weighteither in controls or in patients (FIG. 13A, light and heavy bars ofsimilar height), suggesting that the association between object weightand fingertip forces was not learned yet. However, by the 5^(th) lift,controls clearly scaled the pLFR showing evidence of adaptation, butpatients did not. Both patients and controls lifted a range of weightsin this manner. Then pLFR was correlated with muscle activity for the5^(th) lift. In controls, the pLFR was highly correlated only withactivity in the lifting muscle—the anterior deltoid (aDEL) (FIG. 13B),since the object was lifted by flexing the shoulder. In contrast,patients showed high correlation with activity in many opposing muscles.When patients first practiced lifting the grip object with theirunaffected hand (5 trials) and then with their affected hand (alternatehand strategy), they were able to scale the pLFR on the first trial withthe affected hand with corresponding restoration of selective muscleactivation patterns as in controls (FIG. 13C). This occurred, however,only when the same action (congruent action) was performed with eachhand. These results suggest that successful adaptation to object weightwith the affected hand requires accurate kinesthetic information fromlifting actions. However, when subjects lifted familiar objects ofdifferent weights (e.g. water bottle, soda can) that provided additionaltactile and visual information regarding their weight using thealternate hand strategy, neither controls nor patients transferred theability to scale their pLFR on the first lift with the affected hand.Patients and controls used congruent actions with both hands and wereexplicitly told that the weight of the objects were the same for eachhand. The results suggest that competition among multiple sensorycontexts can interfere with the acquisition of accurate internalrepresentations for adaptation to one sensory context. It is believedthat training protocols for sensorimotor adaptation must initially bestructured within single sensory contexts for successful learning. Theabove results suggest that restoration of adaptation requires accuratemodality specific sensory information, which can be obtained from theunaffected hand.

ii. The Role of Postural Muscle Activation on Movement Efficiency

To understand the role of postural muscle activation on movementefficiency 31 expert pianists with and without symptoms of overuseinjury were studied. Surface EMG was recorded from 14 upper limb muscleswhen they played octaves at baseline and with biofeedback-assistedactivation of the lower trapezius (LT). Symptomatic pianists (n=11)showed reduced activation in the upper and lower trapezius andover-activation in their finger extensor muscle (EDC) at baselinecompared to asymptomatic pianists (n=20) (FIG. 14A). The slope (m)between the EDC and LT (each color cluster represents trials from onesubject) was markedly positive in symptomatic pianists, in contrast to anegative slope in the asymptomatic group, suggesting an inverserelationship between EDC and LT muscles. Voluntary activation of the LTmuscle shifted the slope in the symptomatic group closer to that of theasymptomatic group compared to Baseline (FIG. 14B, see starred plot),suggesting reduced activity in the EDC muscle and greater efficiency offinger movements. Thus, activation of anti-gravity postural muscles,i.e. the lower trapezius, can improve movement efficiency.

Based on the above results, further experiment sought to determine if arelationship exists between lower trapezius activity and excessivegrasping forces. Patients with stroke have been shown to produceexcessive grip forces in both the affected and unaffected hands leadingto grasp inefficiency. As described below in Section 3 the subjects(each color cluster represents trials from one subject) who showedgreater activity in the lower trapezius produced lower grip forces atlift (FIG. 15), i.e. just enough force to hold the object, but not toomuch as to squeeze it, suggesting greater grasp efficiency. Nointervention was applied to voluntarily activate the lower trapezius. Itis believed that grip force magnitude at lift is a meaningful measure ofgrasp efficiency that may be altered by changes in postural muscleactivity.

iii. Feasibility of Alternate Hand Training to Improve Hand FunctionPost Stroke.

Six subjects with post-stroke hemiparesis participated in a 4-weekalternate hand training intervention. Training consisted of eight45-minute sessions conducted twice a week for 4 weeks when patientsgrasped and lifted everyday objects first with their unaffected hand andthen with the affected hand, in a 1:1 alternating manner, by isolatingmovement at the shoulder, elbow or wrist joints. Training was progressedto more difficult grasp orientations, using heavier or lighter objects,and combining grasp and lift with transport and placing movements tosimulate real world tasks. The goal of training was to attainsymmetrical grasping patterns with the two hands. Subjects grasped andlifted an instrumented grip device pre- and post-intervention.Adaptation of fingertip forces to object weight was assessed by thedifference in peak load force rates for the light and heavy weights(FIG. 16A, see also FIG. 13) and clearly improved post intervention.Grasp execution was assessed with the preload phase duration (PLD) whichis the time taken to stabilize grasp and reflects grip-loadcoordination. The PLD has been found to be a robust and sensitivemeasure of grasp impairment post stroke that correlates with severaltests of hand function. Although the patients still had considerableimpairment, the PLD showed significant improvement post intervention inboth the affected and unaffected hands (FIG. 16B). Subjects also showedclinical improvement in tactile sensibility, higher order sensoryintegration (stereognosis and 2-point discrimination), pinch strength,timing on fine motor tasks (8-13) of the Wolf Motor Function Test andquality of life measured with the Stroke Impact Scale (Table 2).

TABLE 1 Improvement with Alternate Hand Training Test Pre (m ± s.e.)Post (m ± s.e.) Comment Monofilament 3.50 ± 0.60 4.30 ± 0.34 Increasedtactile test of sensibility fingertip pressure sensitivity Moberg score:1.73 ± 1.10 0.34 ± 0.15 More objects time to were identified identifyfaster objects by stereognosis (s) Static 2-point 4.44 ± 0.89 2.22 ±0.50 Improved 2-point discrimination discrimination Pinch strength 2.89± 0.78 3.61 ± 0.99 Increased pinch (g/cm²) strength WMFT tasks 42.09 ±13.32 39.09 ± 10.71 Slightly faster 8-13 (s) performance Stroke Impact230.50 ± 9.86  260.50 ± 9.66  Improved quality Scale of life

The above results demonstrate the feasibility of alternate hand trainingfor clinically relevant improvement in hand function.

2. Mechatronic Rehabilitation Device

In one embodiment, the game controller is a mechatronic device foraiding in diagnosis and rehabilitation including forces sensors thatmeasure not only grip forces but load forces. This embodiment of arehabilitation device allows for gathering of information regarding boththe lifting force and the gripping force exerted by the user. In oneembodiment, the rehabilitation device comprises sensors and electronicsfor the purpose of grip and load force scaling, for example flexiforcesensors and Arduino Uno powered by ATMega328 microcontroller.

The rehabilitation device operates on the principles of mechanics, theweight of the body measured by a weighing scale is equal to the netupward normal force acting on the body. Upon application of an upwardforce on the body, the normal force acting upwards becomes the weight ofbody subtracted by the force applied in upward direction. Using thisconcept, the device is capable of measuring the load force, and thecorresponding load force rate.

F _(net) =Mg−F _(p)

-   -   F_(net)=>Net forces acting on the body, M—Mass of the object,        g—gravity, F_(p)—Force applied by patient.

a. Device Construction:

One embodiment of a rehabilitation device 2010 consists of a housing2020, a grasping object 2011, a base 2050, and at least two forcesensors 2030. FIG. 29 illustrates one such embodiment. In a preferredembodiment, there are between two and ten sensors 2030, such asFlexiforce sensors. In a particular embodiment, a force sensor 2030corresponds to each finger of a user's hand. For example, two sensors2030 are 9.53 mm in diameter and the remaining three are 25.4 mm indiameter. The force sensors 2030 comprise two or more types of forcesensors. In the example embodiment with different sized sensors 2030,the smaller sensors are used for grip force measurement 2031 and thelarger sensors are used for load force measurement 2032. In oneembodiment, the load sensors 2032 are included on the grasping object2011 and the load sensors 2032 are positioned on either the base 2050 toreceive the grasping object 2011 or on the grasping object 2011 toengage the base 2050.

In one embodiment, the rehabilitation device 2010 includes a glove 1800that includes the sensors 2031. The glove 1800 may be affixed to thegrasping object 2011 or separate but engageable therewith. A glove isshown in FIG. 18.

In one embodiment, the smaller sensors 2031 are attached to the thumband index finger of the glove 1800. This is adjusted to fit thesubject's fingers accurately using a fastener (such as hook-and-loop)arrangement. This helps the sensors 2031 to coincide with the area ofcontact between fingers and an object. In one aspect, the glove 1800includes components so as to be interchangeable between the two hands ofthe subject. This arrangement satisfies the friction requirement for theappropriate grasping of objects. For embodiments with a glove 1800, thegrasping object 2011 can be an everyday object such as a soda can,coffee cup, ball, etc. Placing the grip sensors 2031 on the glove allowsgrip force data to be gathered without the need for specialized graspingobjects. Placing the load sensors 2032 on the base 2050 allows loadinformation, including weight of the grasping object 2011, to bedetermined without the need for specialized grasping objects.

The base 2050 may include a coaster 2051 for receiving the graspingobject 2011. In one embodiment, the base 2050 includes, such as, in FIG.29A, a triangular coaster 2051 with three legs 2052, which rests on theforce sensors 2035. The base 2050 may rest on a device stand 2060 thatis part of the base 2050. In the embodiment of FIG. 29A, there are threelarger diameter load sensors 2032. These sensors may be of anyacceptable type, including 3-D printed as shown in the embodiment ofFIG. 29A. The grasping object 2011 to be lifted by the patient, such asa soda can or a water bottle, or a systematically weighted apparatus isplaced on top of the base 2050, on the coaster 2051 and the experimentis performed. The legs 2052 may be used to provide an adjustable height,but also serve to focus the load force on the load sensors 2032. Theshape of the coaster 2051 and the number of legs 2052 may be varied asappropriate based on the size, mass, and shape of the intended graspingobject 2011. The load force sensors' 2032 results are summed tocalculate the applied load on these sensors 2032.

In one embodiment of a rehabilitation device using an Arduino board, theoutput voltage of sensors 2030 are connected to an operational amplifiercircuit MCP6004 functioning in inverting mode. The output of op amp'svoltages are measured by the Arduino analog pin. The measured voltagesare converted into corresponding force measurements by Arduino, throughLinear Regression and serially sent for plotting in the computer. TheArduino calculates the corresponding load force rates and grip forcerates that can be plotted in python as well as Matlab®. Therehabilitation device 2010 also facilitates a Parallax LCD display 2090to interactively display the grip forces and load to be lifted. Therehabilitation device 2010 may include a start button and an auditorycue beeper, for example positioned on the base 2050. While a button (notshown) or other manual mechanism for initiating the device 2010 can beprovided, the device 2010 may also be automatically started though theuse of motion sensors, capacitive sensors, or the like, includingthrough the use of sleep and wake modes of operation as standard inmodern electronic devices. The various mechanisms for starting thedevice's 2010 ability to measure forces via the sensors 2030 willgenerally be referred to as “start button”. FIG. 22 illustrates oneexemplary circuit that can be used with the rehabilitation device 2010.

When the start button is pressed, preferably the device 2010 indicatesthe subjects with an auditory cue that the experiment has begun. Thedevice 2010 is smartly programmed to begin the experiment only if theload sensors 2032 detect a grasping object 2011. In one embodiment, thedevice 2011 will only begin an experiment mode if a force equal to orgreater than a threshold, such as 100 grams, is detected. Arduino sensesdata from the connected sensors and converts the voltage readings tocorresponding forces and calculates the load force rates and grip forcerates over 35 ms and returns the data through USB to the computer. Asoftware serial is used to connect the Arduino and Parallax LCD. Thegrip forces applied by the fingers and the remaining load to be liftedby the subject in grams are displayed on the LCD display.

The load sensors 2032 measure the weight of the object. The initialweight of the object is saved as an initial constant. During theexperiment the load sensor 2032 values are subtracted from the initiallysaved weight in order to compute the load force applied by the subject.

The data received through the serial communication is plotted inreal-time, such as with Matlab® and Python® interfaces. A comparativestudy established between Matlab's® sampling rate and Python's® samplingrate found that Python® could sample data faster than Matlab-Arduino IOsoftware interface. Moreover, an end-user interactive gaming interfaceis being developed and Python® is opted to facilitate a gaming interfacedevelopment. The game being developed interactively shows the subjectstheir progress in lifting experiments performed. This type ofinteractive interface also facilitates data extraction in the backgroundfor processing and benchmarking patients' progress in rehabilitationstudies.

The sensors 2030 should be calibrated to provide the best results. Thesensors 2030 exhibit a linear relationship between Force andConductance. With this concept a preliminary design was conceivedconsidering the Resistance-Capacitance timer circuit. This circuitincorporates RCTIME command in Basic Stamp 2e to evaluate theconductance. The Basic Stamp is powered by PIC microcontroller. The peakbaud rate of Basic Stamp was 9600. The sampling rate was lower thanexpected in this design. The circuit is shown in FIG. 23A. For theillustrated circuit:

$\frac{V_{c}(s)}{V_{s}(s)} = {{\frac{1}{{RCs} + 1}\text{=}\text{>}\mspace{14mu} \tau} = {RC}}$

FIG. 23B illustrates a graph of the voltage over time for the circuit ofFIG. 23A.

Moreover, the Resistance Capacitor timing circuit was unable to predictthe loads below 30 grams. This results in an error when the subjectperforms experiment. It is illustrated in the graphs of FIGS. 24B and25A. The circuit illustrated in FIG. 25B is developed to addressperformance issues observed in the existing circuit as evidenced in thegraphs.

It has been observed that the best alternative was to measure thevoltage with no change in current through the sensors 2030. Theseproperties typically are satisfied by operational amplifiers. TekscanCompany recommends utilizing its sensors with MCP6004Operational-amplifiers. The Analog sensors were powered by an invertedvoltage of −4.15V and the output is fed to the inverting Op-amp, thevoltage output from the MCP6004 is read using the analog pins inArduino. Arduino converts the voltage with its inbuilt 10 bit ADCconverters. The data plots between the Force-Voltage concurs with theForce-Voltage linear relationship provided by Tekscan. The mathematicalexpressions relied upon are:

$\mspace{20mu} {{F \propto \frac{1}{R_{s}}};}$where  F  is  force  and  R_(s)  is  resistance  of  Flexi-force  sensor  V_(s) = I_(s)R_(s)  (From  Ohm′s  Law)  I_(s) = constant=>V_(s) = R_(s);$\mspace{20mu} {V_{out} = {{{- V_{s}}*\left( \frac{R_{f}}{R_{s}} \right)\text{=}\text{>}V_{out}} \propto {F.\mspace{20mu} {For}}}}$

Where V_(s) the inverting voltage is applied to the flexiforce sensors.

The measured voltage was linearly regressed using Matlab® and a linearregression plot was obtained. The linear regression is encoded into theArduino Microcontroller to convert the voltages measured intocorresponding force measurement. The regression plots are shown below.The Arduino is powered by an ATMega 328 16 MHz microcontroller. Thisprocessor calculates the load force rates and grip force rates. The datacomputed is serially sent to the computer at 115200 baud rate forplotting.

The described experimental setup was capable of providing accuratemeasurements of 10 grams. The forces observed over a time interval of 35ms are used to calculate the grip force rates and load force rates. Thisaccounts for a sampling rate of 27 Hz.

As the experiment is designed for rehabilitation purposes, theelectronics are designed to restrict the grip force sensors to measure aforce up to 9.8 N 1000 grams, whereas the load sensors can measure up to25 N 2.5 kgs.

The loads measured by summing the load sensors 2032 are linearlyregressed with voltage measurement to obtain the force measurements. Theaccuracy and repeatability of the device 2010 was tested for consecutiveloading and unloading experiments. The accuracy and repeatability wasfound to be under the acceptable limit for the device 2010. In-order tovet the load force applied by the subject, a pulley system was designedto mimic a load applied by the subject. This pulley arrangementfacilitates the loading and unloading of the device. One side of thepulley is loaded on the coaster 2051 and weights can be added andremoved on the other side of the pulley setup. Static friction could beimminent in this pulley system arrangement. Extra care was taken toperform the experiment repetition to make sure static friction wasminimized, so it was neglected in calculations.

For the embodiments of the experiments, the sensors 2030 sample at arate of 2000 Hz. A general concern is to improve the sampling rate ofthis device. It was found that the sampling rate of the device is lower,due to the LCD Display command sequence. Where sampling rate is aconcern, this can be fixed upon implementation of a gaming platformwhere the LCD display can be removed. Further, an external display maybe utilized by a computer in communication with the device 2010. Byremoving the LCD display from the device 2010, such an embodiment isexpected to have a sampling rate with a minimum value of 100 Hz.

3. Statistical Framework for Game Based Rehabilitator.

The described rehabilitator can be used to facilitate rehabilitation.However, actual use in a clinical setting is greatly enhanced if theproper statistical framework is used to interpret data from therehabilitator. Making inference regarding a single subject is animportant goal in clinical and applied settings in health and behavioralresearch. In these fields, researchers are interested in assessingwhether the individual subject's outcome changes between differentconditions such as pre- and post-treatment. Without repeated measures,one can only make a visual judgment regarding the direction andmagnitude of the change. Kazdin recommended performing repeated trialsunder the same condition to reduce the impact of within-subjectvariability using a repeated measure design such as the ABAB type, whereA and B each refer to a different condition. In this case, one canperform a within-subject test evaluating the change using arandomization based test ([14] and [15]). However the classic singlesubject design and method of analysis does not allow researchers tocompare the test subject with a reference population and assess theimpact of between subject variability on the decision.

Described herein is one embodiment facilitating making an inferenceregarding each single subject from a group of test subjects given anavailable training set of subjects whose statuses are known. Instead ofmaking inference about the average behavior of the test set as a group,the status of test subjects is assessed individually or in small groupsand seek to answer questions such as: Does the test subject behave thesame as someone in the healthy population as characterized by thesubjects in the training set?

Note that in this setup, the sample size of the training set may be verysmall. The training and the test sets both have repeated measuresdesign, but the number of trials may not be the same. Moreover theexperimental conditions for the test may only be a subset of thetraining set. Described in the below sections is a novel statisticalframework for testing the above hypothesis in the context of a singlesubject experiment, given a small amount of training data. A simple teststatistic based on sample mean difference between conditions for thetest subject will be compared to a template distribution as a surrogatefor the true sampling distribution of the mean difference under the nullhypothesis. This template distribution is generated based on Bayesianposterior predictive draws using the training data and the test design.

Described below are a practical solution to the statistical testingproblem regarding single-case design, including for use of embodimentsof the rehabilitator described herein. In particular, studies andsimulation were performed to address an important clinical question:does the test patient behave the same as one from the healthypopulation? This question cannot be answered using the traditionalsingle subject design in which only the test subject information isused. Borrowing the concept of training and test sets in machinelearning, we propose using the Bayesian posterior predictive draws ofthe training subject data referenced or generated from the test subjectdesign. This yields a template null distribution of a test statistic forthe purpose of inference prior to actual testing of new subjects. Theperformance of this template distribution can also be studied ahead oftime. It can be used in a clinical situation and physicians can directlycompare the quantity of interest to this distribution to make inferenceat any desired level. The simulation studies have shown that theproposed test performs satisfactorily when compared with its counterparttest in which the true sampling distribution of the test statistics isgiven or known. Moreover, an estimate of the error rate and itsconfidence interval is provided given a single training data set, whichcan further inform physicians about the reliability of the test resultsbased on the given template/experimental design.

Compared to the traditional single-subject design approaches, theproposed method has the following advantages:

-   -   1. It can be easily adapted to test a range of quantities of        interest. Described below are examples with a simple mean        difference based statistic, but physicians may be interested in        using the between trial variability as a measure of performance,        for example. Using the algorithm for deriving template        distribution set forth below, the template distribution of the        between-trial variability can be conveniently produced based on        the between-trial variability of {tilde over (Y)}_(ijt).    -   2. Embodiments of the proposed test are based on a small sample        of training subject data. In general, it is expected that the        training subject data to be a random sample and the experiment        is done in a well-defined laboratory setting. Albeit a small        sample size (for example N=10), a more complex design that        better informs the between-subject and within-subject        variability can be used. Examples herein use a training subject        design with 10 weights and more trial replications. In contrast,        the test subject design is allowed to be simpler so that it is        feasible for patients in a clinical setting.    -   3. Another advantage of the proposed method is that it can be        used to inform single-subject design. Based on the derived error        rates associated with different test designs, researchers and        clinicians can determine ahead of time, an experimental design        for the test subjects with conditions and repeats that optimizes        the error rates and power among all the feasible options.    -   4. Lastly, in a tele-medicine situation, when the physicians are        able to download a template distribution provided from the        research labs and upload clinical data to the lab, the proposed        method will most effectively allow the outcomes in the basic        research laboratory to be quickly applied to clinical setup, and        in addition allows rapid integration of newly collected clinical        data for the purpose of basic research.

The analytic technique provides a crucial component to make sense of thedata gathered by the rehabilitator. Specifically, how the algorithm willbe used by the rehabilitator is illustrated in the followingnon-limiting case examples. The rehabilitator was initially designed totrain and assess the ability to grasp objects of various weights,textures and shapes. Below, examples illustrate a how the algorithm canbe used to assess the use of appropriate grasping forces when the weightof the objects is changed. All the applications can be applied directlyto texture and shape variations as well. In other words, the algorithmwill facilitate a single-subject experiment which will provide not onlythe raw data and averages, but an estimate of the error rate, andfalse-positive and false-negative rate to facilitate decision makingregarding questions such as:

-   -   Is the patient improving or not    -   Should the treatment continue or be changed    -   What is the rate of improvement    -   What is the prognosis for further improvement

Case 1 a (for Weight Sensing Assessment):

The rehabilitator gathers the force data in several dimensions and overa time period, and preprocesses the data to provide a single measure(the peak load force rate, PLFR) which is a measure of learning or theability to predict the weight of the object to be manipulated evenbefore it is lifted. This data will be collected by the mechatronicdevice patented earlier this year. Due to inter-trial variability, thepatients need to perform multiple trials in order to reliably interpretthe data collected. This proposed algorithm will produce a measure thattakes into account the between-trial variability under differentrepeated designs

Case 1b (for Weight Sensing Assessment):

given a reliable estimation of the PLFR, the clinician will be notifiedof a single decision based on a precalculated template regarding whetherthe patient being tested is learning in a HEALTHY manner. The algorithmis based on a rigorous statistical process.

Case 1c (for Weight Sensing Assessment):

For the decision made in 1 b, this algorithm will also provide arigorous assessment on the accuracy of the decision, i.e. if the patientis deemed to be healthy, what is the false negative rate associated withthis decision? If the patient is deemed to be unhealthy, what is thefalse positive rate associated with this decision?

Case 1 d (for Weight Sensing Assessment):

The single subject design has low power because we only have onesubject. This algorithm will provide a post-hoc power analysis to informwhat would be a more optimal assessment design to increase the power(and reduce the error in decision) for the single subject.

Case 1 e (for Weight Sensing Assessment):

The algorithm will allow customization to further improve the validityof such tests. Some of the key parameters (such as different levels ofbetween trial variability) will be allowed to be imported by theclinicians to fit the specific situation of the single subject.

Case 1f (for Weight Sensing Assessment):

The algorithm can also perform assessments of other variables per theclinician's request. For example, the learning curve between trials, thevariability between trials, etc.

Case 1g (for Weight Sensing Assessment):

The data intensive computation will be done behind the scene, ahead oftime. The specific information the rehabilitator needs to conduct allthe aforementioned functions can be implemented using a very simple andfast set of algorithms installed on a computer or built on a smallcomputer chip attached to the rehabilitator as described in the previouspatent for the mechatronic device. The software can be downloaded andupdated conveniently via the internet. The data/results produced by therehabilitator can also be uploaded via the internet to for algorithmupdating.

In one embodiment, the algorithm for use in the statistic framework hasthe following components: 1. A precalculated template: the template iscomputed behind the scene using laboratory collected healthy subjectdata. The calculation of this template will involve a new algorithm thatinvolves a novel application and revision of some known statisticalprocedures. 2. A built-in algorithm in the rehabilitator: this algorithmwill calculate a reliable measure of the PLFR, and use the template tomake assessments regarding the status of the patient, and calculate theerror rate associated with the decision. This decision making process isbased on a new algorithm.

The rehabilitator described in various embodiments above may have acomputer chip to facilitate the data collection, processing andcommunication described herein. For example, in one embodiment a smallcomputer chip can be installed on the rehabilitator, or the algorithmcan be directly installed on a computer that is linked to therehabilitator.

a. Precision Grasp as a Model Task

In post stroke rehabilitation, precision grasp is an important task asit is closely related to patients daily activities. Precision graspdepends on anticipatory control, an ability to predict the optimal forcewhen lifting a familiar object such as a cup of coffee. In healthyindividuals, it has been found that after just one or two practice liftsthe rate of change of load force is faster for a heavier object than fora lighter object ([8] [9]). More specifically, Lu et. al. ([19]) showthat after one practice trial, the Peak Load Force Rate (PLFR) increasesproportionally as the weight of the object being lifted increases.

However, anticipatory control is often impaired among patients withbrain injury due to stroke. Restoring and assessing the ability ofanticipatory control is an important goal in post-stroke rehabilitation.Using a rehabilitator device, such as described above, PLFR can bereadily measured during a designed grasping and lifting task involvingdifferent weights, and it can be used as a convenient clinical measurefor physicians to assess whether each patient is capable of executinganticipatory control during different stages of the rehabilitationprocess.

According to Lu et. al. ([19]), the logarithm of peak Load Force Rateincreases linearly in the object's weight among healthy subjects (see anillustration in FIG. 30). Mathematically, this can be expressed as:

log(PLFR _(i))=α_(i)+βWEIGHT_(it) +E _(it),  (1)

where individual i is lifting weight WEIGHT_(it) on trial t, and E_(it)is idiosyncratic error. The terms α_(i) and β reflect individual-levelbaseline force and population-level (common) effects for differentweights, respectively. Based on a sample of 10 healthy subjects, thescaling factor is found to be 1.4 Newton/ms per 1000 grams weightincrease. Moreover, although individual subjects may have different PLFRwhen lifting, namely different speed in loading force to lift, themanner in which PLFR scales up as a function of weight is fairlyconstant. In other words, in the above linear model, each subject couldbe found to have his/her own intercept (α_(i)), but they all share acommon slope (the scaling factor β).

Under the framework of model (1), assessment of anticipatory control canthen be formulated into the following hypothesis testing problem.

-   -   H0: Patient has healthy anticipatory control The PLFR of the        test subject increases as the weight of the object increases the        same way as in the healthy population. β_(test)=β_(pop).    -   Ha: Patient does not have healthy anticipatory control The test        subject fails to adjust the loading force rate due to weight        changes, hence PLFR does not increase the same way as in the        healthy population, β_(test)<β_(pop)

To test this hypothesis, one needs to estimate the benchmark value forthe scaling factor among healthy population, β_(pop), and the scalingfactor for the test subject, β_(test). Moreover, since PLFR is abehavioral measure, there is substantial trial to trial variability whenthe same subject lifting the same weights for multiple trials, and thereis substantial between subject variability due to individual behavioralidiosyncrasy ([19]). Hence a good statistical test should take intoaccount the uncertainty introduced by both between and within subjectvariability.

b. Data and Existing Approaches

i. The Data

First the data that is typically seen in the grip force example isdescribed. A small training data set of 10 healthy subjects, lifting 10weights ranging 250 to 700 grams, 50 grams apart was used. At eachweight, each subject lifts the object for 7 trials while the first trialis a learning trial where the new weight is presented in a random orderand unknown to the subject. After first trial, healthy subjects arecapable of applying anticipatory control [19] hence a total of 6 learnedtrials. While small in the number of subjects, this is reasonably largein the number of conditions and replications, allowing for preciseestimation of underlying physiological features and their variation.

The test set consists of some stroke patients. Due to time and physicallimitations, each test subject only lifts two to three differentweights; referred to as scenarios one and two, respectively. For eachweight, the test subject performs one practice lift in order to learnthe weight of the object, then repeats for five trials.

ii. A Natural Estimator

In a clinical setup, the most straight-forward way to estimate thescaling factor of the test subject is to simply take the difference inthe peak load force rate measured at different weights, averaged overmultiple trials. If the subject only lifts two weights, a naive samplemean based estimator of the scaling factor is,

${\overset{\_}{\beta}}_{i} = \frac{\sum\limits_{t = 1}^{T}\; \left( {y_{i\; 2t} - y_{i\; 1\; t}} \right)}{T\left( {w_{2} - w_{1}} \right)}$

where y_(i1t) is the PLFR measure for subject i lifting weight one of w₁grams at the tth trial (t=1, . . . , T), and y_(i2t) is thecorresponding measure when subject i lifting weight two of w₂ grams atthe tth trial.

Sometimes, the test subject can be instructed to lift three weights (ormore) of equal distance, in which case, the PLFR can be estimated byaveraging of the differences in PLFR between adjacent pair of weightthen divided by the weight difference per pair. Putting it in a generalformula assuming J weights with equal distance d_(w)=w₂−w₁,

${\overset{\_}{\beta}}_{i} = \frac{\sum\limits_{j = 2}^{J}\; {\sum\limits_{t = 1}^{T}\; \left( {y_{ijt} - y_{{i{({j - 1})}}t}} \right)}}{\left( {J - 1} \right){Td}_{w}}$

Following [19], the scaling factor for healthy population can beestimated using a linear mixed effect model based on the 10 subjects,and they report an estimated benchmark value β_(pop)=1.4 N/ms perkilogram of weight increase. They further prescribe a 95% one-sidedconfidence interval based on the standard error of this estimator:[1.27, ∞). A naive approach is to compare the scaling factor for thetest subject {dot over (β)}_(i) with the estimated benchmark value alongwith this confidence interval. If {dot over (β)}_(i) falls outside theprescribed the confidence interval, the physician can be instructed toreject the alternative hypothesis.

Albeit it simple, this approach is not ideal as 1) the prescribedconfidence interval is too narrow because it is for the mean scalingfactor across a healthy population, and it doesn't take into account ofthe impact of between subject variability for the purpose of comparison;2) the within subject (between trial) variability is not incorporated.

iii. Hierarchical Linear Model Via MLE

An applied statistician might take a modelling approach to compare thescaling factor between the training subjects and the test subject. Sinceeach subject is asked to lift the object for multiple trials and underdifferent weights, the expected outcomes will be clustered/correlatedwithin the same subject and the same weight. As Lu ([19]) pointed out, alinear hierarchical model can be used to model this type of data ([12]).

Since the training data and the test subject use different experimentaldesigns, and a different scaling factor β is expected, the model foreach group was outlined separately using a modified notation. Thefollowing two level linear hierarchical models, substituting Y for log(PF RL) and W for WEIGHT, characterize how the observations are generated:

$\quad\left\{ \begin{matrix}{{Y_{ijt} = {\alpha_{i} + {\beta^{pop}W_{ij}} + u_{ij} + \varepsilon_{ijt}}},} & {{model}\mspace{14mu} {for}\mspace{14mu} {training}\mspace{14mu} {data}\mspace{45mu} (3.1)} \\{{Y_{i^{\prime}j^{\prime}t^{\prime}} = {\alpha_{i^{\prime}} + {\beta^{test}W_{i^{\prime}j^{\prime}}} + u_{i^{\prime}j^{\prime}} + \varepsilon_{i^{\prime}j^{\prime}t^{\prime}}}},} & {{model}\mspace{14mu} {for}\mspace{14mu} {test}\mspace{14mu} {subject}\mspace{56mu} (3.2)}\end{matrix} \right.$

for subjects i in the normal or training population, i=1, . . . , Ntrain, and subjects i_(i) in the test or new population where i_(i)=1 inthe single-subject design. Subscripts t and t indicate the trial numberfor each ^(E)group, t=1, . . . , T and t_(i)=1, . . . , T_(i). W_(ij)and W_(iiji) specify the weight of the objects being lifted. u_(ij) andu_(iiji) specify the subject-weight specific effects and they areassumed to follow N(0, σ₂). The error terms E_(ijt) and E_(iijit) areassumed to be independently distributed N(0, σ₂). Note that commonvariances are assumed for many of the model components, to borrowstrength from the information gained from the training subjects, but tothe extent that test-subject-specific variances wish to be and can beidentified, these assumptions can be relaxed.

Instead of testing hypothesis H₀: β_(pop)=β_(test), an equivalenthypothesis H₀: δ=β_(pop)+β_(test)=0 is considered. The quantity δ can beestimated under the linear hierarchical framework by combining thetraining data and the test subject, specifying an indicator variable,NEWSUBJ_(i), set to 1 if subject i is in the new population and set to 0otherwise. Then, a joint model such as this is fit:

Y _(ijt)=α_(i)+β^(pop) W _(ijt) +δW _(ijt)×NEWSUBJ _(i)+ε_(ijt)  (4)

The above model can be fit using standard statistical software packagessuch as SPSS, SAS, Stata or the nlme package in R (among others) underthe Maximum Likelihood Estimation framework. Fitting this model willproduce point estimates of β_(pop) and δ as well as their standarderrors. One can construct a Wald-type statistic (pointestimator/standard error) to test whether the difference in scalingfactor δ is significantly different from 0 by comparing it with a tdistribution. However, since the Wald test is a large sample basedresult, when there is only one test subject, it is not appropriate touse the t distribution as the null distribution for the test. As analternative, permutation based tests are often used for finite samples,but in this example, there are a total of 11 subjects, so thepermutation based p-value cannot be smaller than 1/11, which implies thepower of the test will be zero if one tries to control the type I errorto be at 5%.

Other issues with MLE approach is that the commonly used softwarepackages tend to be based on strict assumptions about the errorstructure, such as the within-subject variation is the same for thetraining set (healthy subjects) and the test subject (typicallypatients).

iv. Bayesian Hierarchical Linear Model

An alternative to the Maximum Likelihood Estimation is a Bayesianhierarchical model fitting approach [5]. To fit a Bayesian model, oneneeds to first specify a set of prior distributions (and sometimeshyper-prior distributions) for the parameters of interest. The choice ofprior distributions is important as it can have substantial impact onthe model. Since the specification and estimation of Bayesian modelrequire certain level of statistical knowledge, it is less available topractitioners.

To fit (4) under the Bayesian paradigm, one would specify the followingprior and hyper-prior distributions for the training data,

$\begin{matrix}{{p\left( {\beta^{pop},\sigma_{\varepsilon_{1}}^{2}} \right)}\text{\textasciitilde}\frac{1}{\sigma_{\varepsilon_{1}}^{2}}} \\{{p\left( \alpha_{i} \right)}\text{\textasciitilde}{N\left( {0,\sigma_{\alpha_{1}}^{2}} \right)}} \\{{\sigma_{\alpha_{1}}^{2}\text{\textasciitilde}{Inv}} - {{Gamma}\left( {\eta_{1},v_{1}} \right)}}\end{matrix}$

and for the test subject

${p\left( {\beta^{test},\sigma_{\varepsilon_{2}}^{2}} \right)}\text{\textasciitilde}\frac{1}{\sigma_{\varepsilon_{1}}^{2}}$p(α_(i))~N(0, σ_(α₂)²) σ_(α₂)²~Inv-Gamma(η₂, ν₂)

Notice here that the Bayesian model allows us to explicitly specify thevariance components differently for the training data and the testsubject. On the other hand, this setup also allows one to borrowstrength and estimate the variance components jointly by forcing σ_(α) ₁²=σ_(α) ₂ ² and σ_(ε) ₁ ²σ_(ε) _(2′) ².

In one embodiment, non-informative priors were applied to avoid theimpact of prior influence on model estimation [5][2], with the exceptionof the subject-specific intercept variance. Since there are only 10training subjects and 1 test subject, an informative prior, the inversegamma distribution, was applied on the variance of α_(i). This prior(Inv-Gamma(η, v)) is determined by two parameters η and v, correspondingto a prior mean variance value v/(η−1). The choices for v, η, correspondclosely to the MLE estimate of the variance.

The Bayesian approach then estimates the parameters via posteriorsimulation. Let Θ=(β_(pop), β_(new), σ₂, σ₂, σ₂), then the posteriordistribution can be derived using Bayes formula,

$\begin{matrix}{{p\left( {{\Theta \text{|}Y^{train}},Y^{test},W^{train},W^{test}} \right)} = \frac{{p\left( {Y^{train},\left. Y^{test} \middle| \Theta \right.,W^{*}} \right)}{p_{0}(\Theta)}}{\int{{p\left( {Y^{train},\left. Y^{test} \middle| \Theta \right.,W^{*}} \right)}{p_{0}(\Theta)}{\Theta}}}} \\{\propto {{p\left( {Y^{train},\left. Y^{test} \middle| \Theta \right.,W^{*}} \right)}{p_{0}(\Theta)}}}\end{matrix}$

where W*={W_(train), W_(test)} and p₀(Θ) is the prior distribution ofthe parameters. When the closed-form of the posterior distribution isnot available, it can be approximated using Monte-Carlo simulations andused for inference.

For this example, with a simple reparameterization, β_(test)=β_(pop)−δ,the posterior distribution of p(δ|Y_(train), Y_(test)) can be generated.Unlike the MLE approach, in which a single point estimator δ is producedfor the quantity of interest, the posterior distribution of δ is thebasis of Bayesian inference. For example the maximum a posterioriprobability (MAP) value is the δ value corresponding to the peak of theposterior distribution and can be viewed as a Bayesian version of thepoint estimator, the posterior standard deviation is a measure of thevariability in δ (there is no sampling distribution of an estimator;instead, there is a posterior for the corresponding parameter).

However, the Bayesian hypothesis testing does not come naturally due tothe fundamental difference in problem formulation—the Bayesian approachposits that all parameters are random variables and follow adistribution, which is estimated by the posterior distribution, whilethe Neyman-Pearson type of hypotheses typically focus on a singleparameter value. [13] proposed posterior p-value, the Bayesiancounterpart of the classical p-value by simulating the joint posteriordistribution of replicate data and the (nuisance) parameters, bothconditional on the null hypothesis and calculating the tail areaprobability of a “test statistic” using this distribution. However, theposterior p-value has been criticized for its tendency to center around0.5 for hierarchical model [5, 4]. Instead, a shorthand way ofevaluating the posterior probability of δ<0 is considered. Namely, for atest at level 5%, if p(δ<0|Y train, Y test)<0.05, then the nullhypothesis is rejected. In other words, in this instance, there issufficient evidence (95% of the posterior mass) to support δ>0, ordeviation from the training data.

c. Approach to Single-Subject Design Analysis

Unlike directly comparing the naive estimator in equation 2 with apredetermined benchmark value and make a visual judgement about thestatus of the test subject, the use of Maximum Likelihood and Bayesianmodeling allow us to compare the test subject with the training data settaking into account the within-subject and between-subject variability,and statistical tests are available to assist decisions under thesemodelling frameworks. However these approaches are not practical in theclinical setting. In order to make an inference regarding a new subject,one needs to refit the entire multilevel model, which is time consumingand not user-friendly in the clinical setting. Without proper trainingin statistics, these methods are practically unavailable to theclinicians. Moreover since most of the statistical parametric modellingapproach depends on a large sample, the behavior of the aforementionedmethods in hypothesis testing regarding a single subject is unknown. Theerror rates such as false positive and false negative rates associatedwith the decisions can be off target.

To address these concerns, one embodiment uses a novel approach thatwill allow clinicians without any formal statistical training to make aninformed decision about the test subject's status as compared withreference subjects in the training data.

One embodiments starts with the naive estimator β _(new) based onequation 2, which is available. The Bayesian approach handles thenon-asymptotic setting more elegantly, but is inherently more difficultto fit without specialized knowledge of statistical programminglanguages such as STAN, BUGS or JAGS in the clinical setting. The goalis to provide the clinician with a template distribution of the possiblevalues that are expected to be observed given the weight design and thenumber of repeated measures used by the test subject. This templatedistribution is to be developed in a laboratory where the scientists andstatisticians collaborate to design experiments and collect data basedon a carefully controlled set of training subjects, for example, arandom sample of healthy subjects.

Based on the template distribution, the clinician can easily test thehypotheses such as

H0: Patient has healthy anticipatory control β_(new)=β_(pop).Ha: Patient does not have healthy anticipatory control β_(new)<β_(pop)

The probability of observing any values β_(new) as extreme as the naiveestimate β _(new) had the test subject behaved the same way as thereference population can be easily generated using the templatedistribution. This probability has the interpretation of a classicp-value in a statistical inference problem (P(observation asextreme|model) under the null). The clinician can choose a desired levelof the test, say 0.05 and reject the null hypothesis whenever thep-value is less than 0.05. An equivalent alternative is to compare thenaïve estimate β _(new) directly with the critical valueC_(0.05)(β_(new)) derived from the template distribution. If β_(new)<C_(0.05)(β_(new)) then reject the null hypothesis.

Moreover, since the template distribution is derived in a laboratorysetup, the performance of such decisions can be evaluated ahead of time.Along with a convenient test, the expected error rates associated withthe decision will also be reported.

i. The Algorithm for Deriving the Template Distribution

To derive this template distribution, a feature of Bayesian modeling isexploited, which is that posterior predictions are easily computed usingany combination of model parameters. Crucially, this allows one to varythe design between training and test subjects and propagates parameteruncertainty into the predictions, providing a natural framework forstatistical inference that does not rely on asymptotic theory. The stepsfor deriving the template distribution include:

-   -   1. A Bayesian hierarchical model is fit using the training data        set alone to obtain the posterior distribution of the        parameters. For model 3.1 (Hierarchical Linear Model), these        parameters are Θ={α, β^(pop), σ_(α) ², σ_(u) ², σ_(ε) ²}, for        which it is labeled the posterior h(Θ)=p(Θ|Y^(train)).    -   2. Given a new design W_(new), it is assumed, under the null,        that all parameters Θ in model 3.2 (a hierarchical linear model)        are the same. A set of posterior predictive outcomes {tilde over        (y)}˜MVN(μ_(new), Ω_(new)) are generated, where for this model        μ_(new)=a+β_(pop)W_(new) and Ω_(new) has compound symmetry        structure induced by the random effects for intercept and        trials. Specifically,

$\left\{ \Omega^{new} \right\}_{{ijt},{i^{\prime}j^{\prime}t^{\prime}}} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} i} \neq i^{\prime}} \\\sigma_{\alpha}^{2} & {{{{if}\mspace{14mu} i} = i^{\prime}},{j \neq j^{\prime}}} \\{\sigma_{\alpha}^{2} + \sigma_{u}^{2}} & {{{{if}\mspace{14mu} i} = i^{\prime}},{j = j^{\prime}},{t \neq t^{\prime}}} \\{\sigma_{\alpha}^{2} + \sigma_{u}^{2} + \sigma_{\varepsilon}^{2}} & {{{{if}\mspace{14mu} i} = i^{\prime}},{j = j^{\prime}},{t = t^{\prime}}}\end{matrix} \right.$

This posterior, p({tilde over (y)}|Θ, W_(new)) can be viewed as thedistribution of the future outcomes that would be observed, were the newdesign applied to subjects from the training sample. By generating thesepseudo-outcomes in a Bayesian framework, the model uncertainty ispropagated from h(Θ) to the predictions, {tilde over (y)}, representingour current understanding of the physiological process, and this can beupdated should more training subjects become available.

-   -   1. In order to construct the template (reference) distribution,        a large sample is needed of pseudo-subjects drawn from p({tilde        over (y)}|Θ, W_(new)). For any new subject i, take {tilde over        (y)} and compute β using equation (2). This is the natural        single-number (non-parametric) summary statistic one would        compute for a new subject. The density of β over a large number        of such pseudo-subjects is an approximation to the distribution        for a randomly drawn new subject under the null hypothesis.        Given a predetermined false positive rate (FPR), say 5%, the        critical value for rejecting the null hypothesis can be obtained        empirically.    -   2. Recall that β_(new)=β_(pop)+δ. As in step 1, the posterior        predictive draws of {tilde over (y)} under a specific        alternative value of δ, call it δ_(alt) can be obtained by a        location shift of the posterior predictive distribution of        {tilde over (y)} by δ_(alt), again, due to the linearity and        normality of the models examined. Then, following steps 2 and 3,        distributions of β _(alt) are obtained under hypothesized        alternative values of δ_(alt). These distributions can be used        to assess the false negative rate (FNR) or power of the        decisions made regarding to the hypothesis.

d. Simulation Studies

A set of simulations were conducted to assess the performance of theproposed method. The method is applied to the hypothesis testing problemof whether a (new) test subject has healthy anticipatory control. Thistest is based on the naive estimator β _(new) and our Bayesianmodel-based template distribution. As comparison, the performance of aBayesian hierarchical model and the Wald test for the MLE estimator δ ina linear mixed effects model are also tested.

i. Setup

For the basic simulation setup, multiple samples of training data andtest subjects will be simulated according to the following datagenerating process:

-   -   Training data The training data contains 10 subjects simulated        from model (3.1), with the scaling parameter β_(pop)=1.4. The        other parameter values are set as follows, a=2.8, and σα1=0.3,        σu1=0.1 and αE=0.2. This set of parameters are assumed to be the        population parameters of the healthy subjects.

The design matrix for the training data assumes that each subject lifts10 weights, ranging from 250 grams to 750 grams, 50 grams apart. Thesubjects lift each weight for 6 trials (after an initial practice trialthat is discarded).

-   -   Test subject The test subject is also simulated following model        (3.2) with the same parameter values as the training data set        except for βnew. A range of scaling factors βnew from 1.4 to 0.1        will be used, covering both the null and alternative parameter        space. The design matrix used for the test subject is different        from that used in the training set. Since the test subjects will        typically be patients, a less involved weight design and fewer        repeated trials will be used. For test subjects, the number of        trials after practice is set to 5. Two weight design scenarios 1        and 2, defined respectively as:    -   Each subject lifts only two weights at 250 grams and 500 grams    -   Each subject lifts three weights, at 250, 500 and 750 grams

When the test subject is simulated under βnew=βpop=1.4, it correspondsto the null hypothesis. When the test subject is simulated underβnew<1.4, it corresponds to a case within the alternative hypothesisparameter space. The purpose of the simulation studies is to compare theperformance of different methods in terms of false positive rate andfalse negative rate. To estimate false positive rate and false negativerate, a large number of replicates of the training and the test sets aregenerated as necessary.

For each replicate of a simulated data set, consider four different waysof estimating the parameters of interest and testing the null andalternative hypotheses.

Test A: Naive Estimator β and its Sampling Distribution

For a single simulated test subject, βnew was calculated using (2). Thisis denoted as estimator βnew when the test subject is simulated underthe null hypothesis (when βnew=1.4). The distribution of 1000 samples ofβ ₀ ^(new) ^(s) approximates the sampling distribution of denoted byDist(A)· βnew under the null hypothesis, denoted by Dist(A).

A decision rule is proposed using Dist(A): for a one-sided test at levelα, the rejection region is Dα,A={ β:β<Cα,Dist(A)}, where Cα,Dist(A) isthe αth percentile value of Dist(A). By directly by comparing a newsubject's naive estimator β with this rejection region, a decision canbe made regarding a single test subject.

When the hypothetical test subject is simulated using a range of valuesβnew=βpop−δ, where δ=(0.1, 0.2, . . . , 1.3), it forms the alternativehypothesis space. For each value of βnew<1.4 so derived, one estimatethe false negative rate by summarizing test results across 1000 copiesof new test subjects. Note that the false positive rate for this test isa by construction.

To construct this test, a model for the data generating process is used,which us assumed as a close approximation to what would be observed in alarge population of normals. Since the decision rule is based on thesampling distribution of the test statistic βnew under the null, and thenature of this test is non-parametric, one would use the error ratesderived based on this test as the “gold standard” for comparisonpurposes.

Test B: The Estimator {dot over (δ)} and Bayesian-Based TemplateDistributions

Following the method outlined above, Using the R Bayesian package rstan,a first fit model (3.1) is used to train data with non-informativepriors on the hyperparameters, except for σ2. The prior values for thehyperparameters are set to η=5, v=1. Three Markov Chain Monte Carlo(MCMC) chains were run, each of 2000 draws. The first 1000 draws of eachchain are burn-in and are discarded. Based on the Bayesian fit, 3000posterior predictive draws of {tilde over (y)} were generated using thetest subject design. It is important to note here that this approach is“borrowing” the design used in a future observation as a template, butit does not actually use any real future observations in theconstruction of the reference distribution (as opposed to the Bayesianhierarchical modeling method, which does estimate a δ from the testdata). The template distributions are derived following steps 1-4 in theproposed algorithm described above. In particular, the templatedistribution is denoted under the null hypothesis Dist(B).

Note that each set of training data generates a complete template null.This generative process is repeated across different training data tounderstand the variability inherent in the Bayesian analysis. Inpractice, a single template null will be used.

A decision rule is proposed using Dist(B): for a one-sided test at levelα, the rejection region is Dα,B={ β: β<Cα,Dist(B)}, where Cα,Dist(B) isthe αth percentile value of Dist(B). By directly by comparing the naiveestimator β with this rejection region, decision can be made regarding asingle test subject. Similarly, the error rates associated with thistest can be summarized when using different true βnew values for thealternative.

This is also the method proposed for the physicians to use in theclinical set-up. In this case, the empirical error rates are notavailable, but they can be calculated based on examining the overlappingareas between the template distributions under the null and under aspecific alternative parameter value. This slightly different simulationdesign is called Dist(B*).

Test C: Bayesian Posterior p-Value

For each pair of training set and single test subject, a joint Bayesianhierarchical model (4) is fit. Based on the posterior distribution of δ,one can compute the probability p(δ≦0|Y train, Y test)=p(βnew≧ρpop|Ytrain, Y test). This probability can be interpreted as a p-value, underthe Bayesian framework as the “support” of the hypothesis δ=0 under aone-sided alternative (when the support drops below 0.05, say, thesupposition that 5 is not positive is rejected). Across 300 copies ofsimulated datasets for each alternative (paired with test sets), thefalse negative rate of the posterior p-value based test can be estimatedand averaged.

Test D: Wald Test for MLE Estimator δ

For each pair of training set and single test subject, the difference inscaling factor is estimated between the test subject and the population(as represented by the training data) using model equation 4, whichyields the difference estimate δ. The R package nlme can be used to makethis estimate. The nlme package also reports a Wald test statisticδ/SE(δ), and a p-value is calculated based on this test statisticassuming it has a t distribution under the null with a degree freedombased on the hierarchical linear model framework. The result of thistest at level α is also summarized across 1000 pairs of simulatedtraining set plus a single test subject to approximate the falsepositive and false negative rates.

ii. Results

Since in practice, the distribution of test subjects under thealternative is not known, the use of “gold standard” test subjects is anidealized evaluation, which is reported for Test B. The true error ratesfor Test B are not directly obtainable, but a closely related isevaluated, and more practical test referred to as B. For this test, theexpected false negative rate will be calculated based on the overlappingarea between the template distributions under the null and the analternative constructed via a location shift of the null, which isobtainable. The model-based results Test C and Test D will also beassessed to understand the behavior of these tests in small samples.

Table 3 shows the simulated error rate under various hypothesizedδ₀=β_(pop)−β_(test) values using tests A-D. In the first row when δ₀=0(the first row), the test subjects are generated under the nullhypothesis space, hence the corresponding quantities are False PositiveRates (FPR) of different tests. They are also the type I error rate.When δ₀>0, the test subjects are generated under the alternativehypothesis space hence the remaining rows report the false negativerates (FNR or 1-power).

Focusing first on Test A for Scenario One when two weights are used,with a level 0.05 test, it can be seen that the FPR is 0.05 byconstruction (the critical value was based on the 5th percentile on thesame distribution). FNRs are quite high at 46.1% even for a very strongalternative, as is given by the last entry (δ₀=1.3). This is to beexpected with a small test sample and a low FPR. Continuing to observeTest A, it can be seen that under Scenario Two to the right when threeweights are used, the False Negative Rate decreases substantially. Atδ=0.7, when the scaling factor of the test subject is half of that ofthe expected value of normal training subjects, the FNR is 39.1%. Atgreater δ values, the extra weight condition makes a big difference interms of reducing the false negative rate and increasing the potentialpower of this test.

In comparison, the error rates of our proposed method Test B, using onlythe training data and the test design, are only slightly higher thanthose of Test A when the true sampling distribution of the scalingfactor βnew is assumed to be known. In particular, the false positive ofTest B is 5.6% suggesting that our proposed test is capable ofcontrolling the type I error at the desired level.

Since in practice the expected error rates of Test B are unknown, undercolumn Test B*, error rates are approximated by calculating theoverlapping area of the null template distribution and the alternativetemplate distribution when δ=δ₀ (derived using the method in forderiving template distribution described above, step 4). Both tests Band B*yield strikingly similar FPRs and FNRs under our range ofscenarios, and these are also quite close to what the standard in Test Awould expect. In addition, the standard error of Test B's and Be's FNRscan be estimated, which are found to range from 0.02 to 0.06 in nearlyevery instance, with smaller s.e. for Scenario Two. The availability ofthese standard errors allows us to report, in a practical setting, anestimated error rate with confidence interval for Test B using thosequantities calculated under Test B*.

TABLE 2 The comparison of error rates for different tests. The firstcolumn shows the true values of δ₀ under which test data were generated.Two desired levels of test are considered, 5% and 10%. The first rowshows the false positive rate of different tests. The remaining rowsshow the false negative rate since δ₀ > 0. Since Test B is to be used inthe clinical setting, the expected false negative rate associated with agiven level of the test are shown in column “Test B*”. Scenario OneScenario Two δ₀ Test A Test B Test B* Test C Test D Test A Test B TestB* Test C Test D Level of Test: 5% 0.0 0.050 0.056 0.050 0.053 0.2500.050 0.043 0.050 0.043 0.326 0.1 0.934 0.936 0.935 0.916 0.660 0.8950.922 0.917 0.933 0.623 0.2 0.915 0.928 0.916 0.900 0.609 0.835 0.8680.870 0.876 0.552 0.3 0.893 0.882 0.895 0.870 0.597 0.777 0.785 0.8070.806 0.467 0.4 0.859 0.871 0.869 0.846 0.535 0.673 0.722 0.729 0.6900.451 0.5 0.828 0.841 0.839 0.813 0.486 0.592 0.633 0.637 0.593 0.3600.6 0.792 0.820 0.804 0.800 0.431 0.488 0.530 0.537 0.510 0.256 0.70.754 0.763 0.766 0.750 0.357 0.391 0.421 0.435 0.400 0.224 0.8 0.7150.732 0.724 0.660 0.343 0.287 0.338 0.337 0.293 0.179 0.9 0.668 0.6660.678 0.590 0.316 0.202 0.246 0.250 0.206 0.136 1.0 0.618 0.633 0.6300.536 0.251 0.133 0.160 0.177 0.110 0.086 1.1 0.581 0.592 0.579 0.4800.219 0.080 0.112 0.119 0.073 0.072 1.2 0.516 0.546 0.527 0.416 0.1930.052 0.069 0.077 0.043 0.044 1.3 0.461 0.455 0.475 0.380 0.156 0.0290.044 0.047 0.020 0.042 Level of Test: 10% 0.0 0.100 0.103 0.100 0.1100.307 0.100 0.086 0.100 0.073 0.366 0.1 0.879 0.868 0.875 0.866 0.6140.824 0.848 0.846 0.863 0.566 0.2 0.852 0.851 0.846 0.836 0.562 0.7390.772 0.777 0.766 0.513 0.3 0.818 0.797 0.812 0.810 0.527 0.669 0.6630.692 0.670 0.437 0.4 0.768 0.769 0.775 0.790 0.478 0.551 0.595 0.5960.580 0.403 0.5 0.726 0.739 0.734 0.740 0.441 0.467 0.496 0.494 0.4830.314 0.6 0.681 0.696 0.689 0.660 0.379 0.355 0.390 0.393 0.370 0.2180.7 0.629 0.638 0.641 0.583 0.304 0.273 0.297 0.298 0.260 0.184 0.80.595 0.600 0.590 0.526 0.296 0.183 0.211 0.216 0.170 0.147 0.9 0.5390.528 0.539 0.453 0.273 0.124 0.141 0.150 0.103 0.111 1.0 0.485 0.4810.486 0.406 0.218 0.079 0.082 0.099 0.066 0.073 1.1 0.445 0.450 0.4340.340 0.179 0.042 0.060 0.062 0.036 0.058 1.2 0.381 0.400 0.384 0.2800.153 0.027 0.030 0.037 0.020 0.036 1.3 0.328 0.334 0.335 0.240 0.1240.013 0.021 0.021 0.013 0.033

With only one test subject, it is expected the power of the test will below. However, when the level of the test is set to be 10%, the falsenegative rate is greatly reduced. When δ>0.7, the False Negative Rateunder Scenario Two for Test A is less than 20%, corresponding to a powerof at least 80%, and the performance of Test B and Test B* continue tobe very similar.

The performance of model-based tests C and D are also assessed in Table2. Surprisingly, it find that Test D, the classical mixed effects modelapproach, fails to control the type I error rate at the proper level.For example, under scenario one, the false positive rate for Test D is25% while the desired rate is 5%. This suggests that the Wald testrelies strongly on asymptotic behavior and it is not appropriate forsample sizes associated with single-case design.

Test C, on the other hand, does a better job at controlling the FPR andmanages to achieve FNRs that are comparable to the standard set by TestA. Since the p-values of Test C are evaluated empirically based on theposterior distribution of δ, it is free from the small sample “curse.”Indeed, the fully Bayesian hierarchical model performs a little betterthan Test A with respect to FNR under scenario one, which is when theinformation collected around test subject is more limited. This suggeststhat, if modelled under the correct data generating process, by jointlymodeling the training data (under a stronger design) and the testsubject, a slightly higher powered test can be attained.

FIGS. 31A and 31B compare the sampling null distribution of δ (Dist(A))and the proposed alternative null distribution based on Bayesianposterior predictive draws (Dist(B)). Since multiple samples of Dist(B)are available, the variability inherent in model estimation can bedisplayed by superimposing 100 samples of this distribution upon thestandard Dist(A). The left panel plots the results based on thetwo-weight design, and the right panel plots the results based on thethree-weight design. As can be seen, the two distributions are fairlyclose, and the variability in Dist(B) is surprisingly small, consideringthat it is estimated from only 10 normal subjects. This is because thetraining data uses a stronger within-subject design where each subjectlifts 10 different weights with more repeats.

What is claimed:
 1. An system for providing therapy or training,comprising: a wearable apparatus comprising a sensor; a vibro-tactileactuator; a wireless transmitter; wherein the vibro-tactile actuator ispositioned within the wearable apparatus to be adjacent to a muscle ofinterest for providing therapy.
 2. The system of claim 1 wherein thesensor includes a first proximity sensor and a second proximity sensor,the first proximity sensor associated with a limb portion of thewearable apparatus and the second proximity sensor associated with atrunk portion of the wearable apparatus, wherein the wearable apparatusis configured to provided an indication of when and how far the limb isaway from the trunk.
 3. The system of claim 1 wherein the vibro-tactileactuator is associated with a shoulder portion of the wearable apparatusand is configured to provide vibro-tactile feedback when the firstproximity sensor and second proximity sensor indicate the elbow is awayfrom the trunk.
 4. The system of claim 1, further comprising a gamecontroller.
 5. The system of claim 4, wherein the game controllercomprises a game controller sensor.
 6. The system of claim 5, whereinthe game controller sensor comprises a force sensor and an orientationsensor.
 7. The system of claim 5, wherein the wearable apparatus is incommunication with the game controller.
 8. The system of claim 4,wherein the game controller comprises: a housing having a shape; a firstforce sensitive resister and a second force sensitive resistorpositioned on opposite sides of the housing; an acceleratometer; agyrometer; and a wireless transmitter.
 9. The system of claim 8, whereinthe game controller is a glove and further wherein the housing is aflexible material.
 10. The system of claim 9, further comprising avibro-tactile actuator.
 11. A computer-implemented machine for trainingor rehabilitating, comprising: a coaster configured to receive one ormore grasping objects; a base having one or more load force sensors forreceiving force from the coaster; a glove having two or more grip forcesensors; a processor; and a tangible computer-readable mediumoperatively connected to the processor and including computer codeconfigured to: receive information from the one or more load sensors andthe two or more grip force sensors; and determine a change in load forceand a grip force exerted through the glove.
 12. The computer implementedmachine of claim 11, wherein the computer code is further configured todetermine if the change in load force and the group force are eachwithin an identified range of acceptable states and if not, sendinformation to the feedback mechanism to provide corrective feedback.13. A method of treating neurological injury comprising: training anaffected hand using modality-specific information from an unaffectedbody part which will stimulate activation of similar muscle groups onthe affected body part.
 14. The method of claim 13 further comprisingcalculating a scaling error associated with the difference between theforce needed to grasp an object and the force actually used by theaffected hand to grasp the object.
 15. The method of claim 14, whereinif the scaling error is greater than 50%, providing a signal indicatingpractice is to be performed with the unaffected hand.
 16. The method ofclaim 13, further comprising calculating a trial-to-trial variability inthe magnitude of a load force for the affected hand and for theunaffected hand.
 17. The method of claim 16, further comprisingdetermining if trial-to-trial variability is greater than 50% more forthe affected hand than the unaffected hand and, if so, triggeringstimulation of a feedback mechanism indicating activation of astabilizing muscle.